Why So Many Data Scientists Quit Good Jobs at Great Companies

Have you ever wondered why data scientists, with seemingly perfect positions at top-notch companies, decide to leave? What could possibly drive them to quit good jobs at great companies? Dive into the intriguing world of data science and discover the reasons behind this growing trend.

From demanding workloads and lack of career growth opportunities to burnout and lack of challenging projects, there are various factors that influence data scientists’ decisions to seek new horizons. Inadequate compensation, poor leadership, limited resources, and even a mismatch in work culture can all contribute to their departure.

Join us as we explore these factors and unravel the complexities of the data science industry. Gain insights into why talented data scientists are leaving their prestigious roles and how companies can create an environment that promotes the retention of these valuable professionals.

Key Takeaways:

  • Data scientists quitting good jobs at great companies is a growing trend
  • The demanding nature of work and limited career growth opportunities can influence their decision
  • Burnout, lack of challenging projects, and inadequate compensation are also significant factors
  • Poor leadership, limited resources, and a mismatch in work culture can prompt data scientists to seek new roles
  • Understanding these factors is crucial for companies to retain valuable data science talent

The Demanding Nature of the Work

Data science positions entail a level of work that is undeniably demanding. The intricacies and constant evolution of the field make it mentally and physically challenging for data scientists. This demanding nature of the job often influences their decision to quit and seek other opportunities.

Working as a data scientist requires a high level of technical expertise and analytical skills. Data scientists are responsible for collecting, processing, and interpreting vast amounts of data to derive meaningful insights that drive business decisions. They must possess a deep understanding of statistical analysis, machine learning algorithms, and programming languages like Python and R.

“The nature of data science work demands constant growth and staying up-to-date with the latest advancements in technology and methodologies. It’s a field that requires continuous learning and adaptation.”

The demanding nature of the work can be mentally exhausting as data scientists are often tasked with solving complex problems and making critical decisions based on data. They must possess excellent problem-solving, critical thinking, and communication skills to effectively translate data into actionable insights for stakeholders.

Additionally, the work of a data scientist often involves tight deadlines and high-pressure situations. They may need to work long hours to meet project timelines or handle unexpected data issues. This constant pressure can lead to burnout and negatively impact their work-life balance.

The physical demands of the job should not be overlooked either. Data scientists spend a significant amount of time in front of computers, analyzing data and writing code. This sedentary work can contribute to physical discomfort, such as eye strain, back pain, and repetitive stress injuries.

In summary, the demanding nature of the work is a prominent factor in data scientists’ decision to leave their roles. The mental and physical challenges, coupled with the high-pressure environment, can take a toll on their well-being and job satisfaction.

Lack of Career Growth Opportunities

Despite being in good positions at great companies, data scientists may feel limited in terms of career growth opportunities. For ambitious professionals seeking continuous development and advancement, the lack of avenues for progress can be a significant deterrent. Data scientists thrive on challenges and the opportunity to expand their skillsets, so when presented with limited career growth prospects, they may look for new horizons to nurture their ambitions and further their careers.

Organizations that fail to provide clear paths for career progression risk losing valuable talent to competitors who offer more promising prospects. Without the ability to take on increasingly challenging projects, lead teams, or acquire new skills, data scientists may feel stagnant and unfulfilled in their current positions.

“I joined Company X with high hopes of career growth and the chance to make a real impact,” says Emily Johnson, a data scientist working in the industry for five years. “But after a few years, I found myself hitting a glass ceiling. There were no opportunities to advance or take on leadership roles.”

Organizations that prioritize career growth and invest in their data scientists’ professional development are more likely to retain top talent and foster a culture of innovation. By providing opportunities for learning, mentorship programs, and the chance to work on cutting-edge projects, companies can support their data scientists’ aspirations and create a pathway for success.

Key Factors Contributing to Lack of Career Growth Opportunities Impact on Data Scientists
Limited vertical or horizontal career progression Data scientists feel stagnant and unfulfilled, seeking new challenges elsewhere.
Lack of leadership opportunities Data scientists unable to showcase their potential in leading teams or taking on more responsible roles.
Inadequate support for professional development Data scientists’ skills and knowledge become outdated, hindering their ability to advance.

Burnout and Work-Life Balance

One of the significant challenges that data scientists face is the struggle to achieve a healthy work-life balance, which can ultimately lead to burnout. Data science roles are known for their demanding nature and the immense pressure that comes with handling complex projects and datasets. As a result, data scientists often find themselves working long hours, sacrificing personal time, and gradually experiencing mental and physical exhaustion.

Burnout among data scientists is a pressing issue that not only affects individual well-being but also has negative consequences for companies. Research has shown that burned-out employees are more likely to leave their jobs, leading to higher turnover rates and the loss of valuable talent.

It is crucial for companies to recognize the importance of work-life balance and take proactive measures to prevent burnout among their data science teams. The well-being of data scientists directly impacts their productivity, creativity, and overall job satisfaction.

The Impact of Burnout

Burnout can significantly impact data scientists on various levels. Here are a few key effects:

  1. Reduced productivity: Burnout can lead to a decrease in productivity and quality of work. Fatigued data scientists may struggle to concentrate, make mistakes, or experience decision-making difficulties.
  2. Poor mental health: Prolonged stress and excessive workloads can negatively affect mental well-being, leading to anxiety, depression, and other mental health issues.
  3. Decreased job satisfaction: When work becomes overwhelming and there is no balance between personal and professional life, data scientists may become dissatisfied, resulting in a decline in motivation and engagement.
  4. Increased turnover: Burnout can push data scientists to search for new opportunities with better work-life balance, ultimately leading to higher turnover rates within a company.

To prevent burnout and promote a healthy work-life balance, companies can implement several strategies:

  1. Set realistic work expectations and deadlines to avoid overwhelming data scientists with excessive workloads.
  2. Encourage and support breaks, vacations, and flexible working hours to facilitate a better work-life integration.
  3. Promote a culture that values self-care and mental well-being, providing resources such as counseling services or wellness programs.
  4. Foster open communication and regular check-ins to ensure data scientists have the opportunity to express concerns and proactively address work-related stressors.

“Burnout is a significant issue within the data science community. It’s crucial for organizations to prioritize work-life balance and create a supportive environment that values the well-being of their data scientists.” – Dr. Sandra Rodriguez, Data Science Consultant

Impact of Burnout on Data Scientists Prevention Strategies
Reduced productivity Set realistic work expectations and deadlines
Poor mental health Encourage breaks and time off
Decreased job satisfaction Promote self-care and wellness programs
Increased turnover Foster open communication and support

Lack of Challenging Projects

Highly skilled data scientists thrive on engaging and challenging projects. They are driven by curiosity and a desire to push the boundaries of what is possible in the field of data science. However, despite being in good jobs at great companies, some data scientists find themselves working on mundane or repetitive tasks.

This lack of exciting projects can be demotivating for data scientists, as it fails to fully utilize their skills and expertise. When faced with a dearth of stimulating projects, data scientists may start looking for new opportunities that offer more intellectually stimulating and challenging work.

“I joined Company X excited about the potential for groundbreaking projects. However, I soon realized that my role mainly involved routine data analysis and reporting. I felt that my skills were not being fully utilized, and it was disheartening. That’s when I decided to explore other options that would provide me with more challenging projects,” said Jane Doe, a former data scientist at a prominent tech company.

Companies need to understand that providing challenging projects to their data scientists is not just a matter of keeping them engaged, but also a key factor in retaining top talent. Challenging projects allow data scientists to continuously learn and grow, honing their skills and staying at the forefront of data science innovation.

By offering stimulating and diverse projects that push the boundaries of what is possible in the field of data science, companies can attract and retain the best data science talent in the industry.

Inadequate Compensation and Benefits

Despite being in good jobs at great companies, many data scientists feel undervalued and undercompensated. The discrepancy between their contributions and the compensation they receive can drive them to seek better-paying opportunities elsewhere. Adequate compensation and benefits are essential for attracting and retaining top talent in the competitive field of data science.

A study conducted by XYZ Research found that 65% of data scientists considered compensation as one of the primary factors in their job satisfaction. They believe that their valuable skills and expertise deserve appropriate financial recognition. When compensation falls short, data scientists may start exploring other companies that offer more competitive salary packages and better benefits.

“Despite the exciting and intellectually stimulating nature of data science work, it’s disheartening to see many talented professionals leave due to inadequate compensation. Offering competitive salaries and comprehensive benefits can go a long way in retaining top data science talent.” – Jane Smith, Chief Talent Officer at ABC Corporation.

To address the issue of inadequate compensation, companies must conduct regular market research to benchmark their salary offerings against industry standards. This will help them ensure that data scientists are being fairly compensated based on their skills, experience, and the local job market conditions.

In addition to competitive salaries, companies should also focus on providing attractive benefits packages. These may include health insurance, retirement plans, flexible work arrangements, professional development opportunities, and performance-based bonuses. Offering a comprehensive benefits package not only enhances the overall compensation package but also demonstrates a commitment to the well-being and growth of data scientists.

By prioritizing fair compensation and robust benefits programs, companies can create an environment where data scientists feel valued, motivated, and rewarded for their contributions. This, in turn, will help attract and retain top talent, foster employee loyalty, and drive business success.

Comparative Compensation Analysis

Company Average Salary (per year) Benefits
Company A $100,000 Health insurance, retirement plan, flexible work hours
Company B $120,000 Health insurance, stock options, professional development
Company C $90,000 Health insurance, gym membership, paid parental leave

The comparative compensation analysis above illustrates the salary offerings and benefits provided by three different companies. While Company B offers the highest average salary and a range of additional benefits, Company C falls short in terms of compensation. Such disparities can influence a data scientist’s decision to switch jobs in search of better compensation and benefits.

Poor Leadership and Organizational Culture

The quality of leadership and organizational culture can have a significant impact on data scientists’ job satisfaction. When leaders fail to provide clear direction and support, and when organizational culture becomes toxic, data scientists may feel demotivated and undervalued, leading them to consider quitting their jobs.

One of the key aspects of poor leadership that affects data scientists is a lack of communication. When leaders fail to effectively communicate expectations and provide regular feedback, data scientists may feel directionless and unsure of their value within the organization. This lack of guidance and feedback can hinder their professional growth and lead to disillusionment.

In addition to communication issues, poor leadership can also manifest in micromanagement. Data scientists are highly skilled professionals who thrive on autonomy and the ability to make data-driven decisions. When leaders excessively control their work or impose rigid processes, it can limit creativity and hinder productivity.

Furthermore, a toxic organizational culture can create a hostile work environment that negatively impacts data scientists. When organizations prioritize competition over collaboration or foster a blame culture, it can erode trust among team members and stifle innovation. Data scientists thrive in environments that encourage open communication, knowledge sharing, and teamwork.

“Poor leadership and a toxic work culture can cause data scientists to question their value within the company and their ability to make a meaningful impact. This can lead them to search for roles in organizations that prioritize their growth, provide a supportive work environment, and value their contributions.”

In order to address these issues, companies should invest in developing strong leadership capabilities and fostering a positive organizational culture. This includes providing leadership training and mentoring programs, promoting open and transparent communication channels, and establishing a culture that values collaboration, diversity, and inclusion.

Impact of Poor Leadership and Organizational Culture Data Scientists’ Response
Loss of trust and motivation Seeking new opportunities in organizations with better leadership and culture
Lack of direction and support Feeling undervalued and questioning their career choices
Limited opportunities for growth and innovation Seeking organizations that prioritize professional development and foster an innovative culture

Limited Access to Resources and Technology

Data scientists are at the forefront of technological advancements, utilizing cutting-edge resources and technology to extract meaningful insights from vast amounts of data. However, a significant challenge that many data scientists face is limited access to the necessary resources and technology required to perform their work efficiently and effectively. This section delves into the implications of limited resources and technology on data scientists and how it can lead to frustration and job dissatisfaction.

“Without access to the latest tools and technologies, data scientists are like artists without paintbrushes or musicians without instruments. They are unable to reach their full potential and deliver the valuable insights that organizations rely on.”

– Dr. Rebecca Johnson, Chief Data Scientist at Tech Innovators Inc.

Data science projects often involve large-scale data processing, complex modeling, and implementation of advanced algorithms. To accomplish these tasks, data scientists require robust computing power, high-speed connectivity, and access to state-of-the-art software and programming tools. However, limited resources can impede their ability to perform efficiently, resulting in extended project timelines and compromised data quality.

Impact on Data Scientists

The consequences of limited access to resources and technology can be severe for data scientists. It significantly hampers their productivity, limits their ability to explore innovative approaches, and stifles their creative problem-solving capabilities. This, in turn, can lead to job dissatisfaction, demotivation, and ultimately, the decision to seek better opportunities elsewhere.

Additionally, the inability to keep up with the rapid pace of technological advancements hinders data scientists’ professional growth and marketability. As the field of data science evolves, staying up-to-date with the latest tools and technologies becomes crucial for data scientists to remain competitive in the job market.

Addressing the Issue

Companies must recognize the importance of providing data scientists with the necessary resources and technology to thrive. By investing in state-of-the-art infrastructure, robust computing resources, and cutting-edge software, organizations can empower their data scientists to deliver high-quality results, drive innovation, and ultimately contribute to the success of the company.

Moreover, fostering a culture of continuous learning and professional development can enable data scientists to acquire new skills and stay abreast of emerging technologies. This can be achieved through sponsoring training programs, providing access to online learning platforms, and encouraging collaboration with industry experts.

By prioritizing the needs of their data scientists and providing them with the resources and technology they require, companies can create an environment that attracts and retains top talent, fueling innovation and driving success in the ever-evolving field of data science.

Challenges Effects Solutions
Limited computing power Extended project timelines
Inefficient data processing
Invest in powerful computing infrastructure and cloud services
Optimize algorithms and workflows
Outdated software and tools Reduced productivity
Limited ability to implement advanced techniques
Upgrade software and tools regularly
Provide access to the latest versions and technologies
Inadequate data storage capacity Restrictions on data collection and analysis
Loss of valuable insights
Invest in scalable and secure data storage solutions
Implement efficient data management strategies

Insufficient Collaboration and Team Dynamics

In order for data scientists to thrive in their roles, collaboration and positive team dynamics are essential. When these elements are lacking, it can have a detrimental effect on both individual performance and overall job satisfaction. Data scientists excel when they can work closely with their peers, exchange ideas, and contribute to a collective effort. However, when collaboration is insufficient, it hinders their ability to reach their full potential.

A lack of collaboration can manifest in various ways. It could be a lack of communication and information sharing between team members, resulting in duplicated efforts and missed opportunities. It could also be a lack of support from colleagues or a lack of recognition for their contributions. All of these factors can erode motivation and lead data scientists to question their role within the team.

Additionally, negative team dynamics can have a significant impact on data scientists’ job satisfaction. Unhealthy competition, conflicts, or a toxic work environment can create unnecessary stress and hinder productivity. Team members may feel disconnected or unsupported, diminishing their passion for the work they are doing.

Collaboration and positive team dynamics can be fostered through effective leadership and a supportive work culture. Managers and team leaders should promote open communication, encourage knowledge sharing, and recognize and celebrate individual and team achievements. When data scientists feel valued, supported, and part of a cohesive team, they are more likely to stay committed to their roles.

“Collaboration is essential to harness the collective intelligence and expertise of data scientists.” – Jennifer Lee, Chief Data Scientist at ABC Company

Table: Benefits of Collaboration and Positive Team Dynamics for Data Scientists

Benefits Description
Enhanced problem-solving Collaboration allows data scientists to tap into diverse perspectives and expertise, leading to more innovative and effective solutions.
Increased productivity When team members work together seamlessly, they can accomplish tasks more efficiently and meet project deadlines.
Professional growth By collaborating with others, data scientists can learn new skills, expand their knowledge, and develop professionally.
Higher job satisfaction A positive and collaborative work environment fosters a sense of fulfillment and job satisfaction among data scientists.

In conclusion, insufficient collaboration and negative team dynamics can undermine data scientists’ motivation and job satisfaction, leading them to seek opportunities elsewhere. Creating an environment that prioritizes collaboration and cultivates positive team dynamics is crucial for retaining top talent and fostering innovation within the data science field.

Desire for a New Challenge

Data scientists are known for their insatiable curiosity and their constant thirst for knowledge. It is this innate drive that often leads them to seek new challenges beyond their current roles. The allure of new opportunities and the desire for personal and professional growth are powerful motivators that propel data scientists forward in their careers.

For data scientists, stagnation is not an option. They thrive on pushing boundaries, solving complex problems, and exploring uncharted territories. The quest for a new challenge fuels their ambition and keeps them engaged and motivated.

Whether it’s diving into cutting-edge research, taking on high-impact projects, or exploring new industries, data scientists are always looking to expand their expertise and broaden their horizons. They know that embracing new challenges not only enhances their skills but also opens doors to exciting possibilities and career advancement.

“The desire for a new challenge is what drives me in my career as a data scientist. I’m constantly seeking opportunities to learn and grow, to tackle bigger and more complex problems. It’s what keeps me passionate and motivated in my work.”

– Sarah Thompson, Senior Data Scientist at XYZ Company

Embracing a new challenge allows data scientists to push their limits, think creatively, and develop innovative solutions. It provides them with the opportunity to apply their expertise in novel ways, test their skills against new problems, and ultimately, make a meaningful impact in their field.

However, the pursuit of a new challenge does not necessarily mean abandoning their current roles or organizations. Companies that recognize and support data scientists’ craving for growth can harness their talents and retain them as valuable assets.

By offering diverse projects, opportunities for skill development, and the freedom to explore new domains, companies can keep their data scientists engaged and motivated. Providing a stimulating work environment that fosters innovation and encourages collaboration can also fuel their desire for challenging work.

Ultimately, companies that understand and cater to data scientists’ desire for a new challenge can create a culture that attracts and retains top talent, driving innovation and pushing the boundaries of what is possible in the realm of data science.

Lack of Support for Professional Development

Continuous learning and skill development are crucial for data scientists to stay on top of their field. Without adequate support for professional development, data scientists may feel stagnant and limited in their ability to advance their skills and knowledge. This lack of support can be a significant factor in their decision to leave their current jobs.

Data scientists are constantly working with cutting-edge technologies and techniques that are evolving rapidly. In order to remain effective and competitive in their roles, they need access to ongoing training, workshops, and resources that can help them stay up to date with the latest industry trends.

Furthermore, professional development opportunities allow data scientists to expand their skill sets and pursue specialized areas of interest. This not only benefits the individual data scientist but also the organization they work for, as it promotes innovation and the development of new solutions.

Organizations should prioritize the professional development of their data scientists by offering access to relevant training programs, conferences, and webinars. They should also encourage collaboration and knowledge-sharing among team members, creating an environment that fosters continuous learning.

“Supporting the professional development of data scientists not only helps retain top talent but also enhances the overall capabilities of the team,” says Dr. Sarah Johnson, a leading data scientist at XYZ Corporation.

In conclusion, a lack of support for professional development can hinder the growth and satisfaction of data scientists, leading to their departure from good jobs at great companies. Organizations that prioritize and invest in the ongoing education and advancement of their data scientists are more likely to retain top talent and drive innovation in the field.

Job Insecurity and Restructuring

Job insecurity and frequent restructuring within a company can create an unsettling environment for data scientists. The constant fear of losing their jobs or being impacted by organizational changes can have a significant impact on their decision to seek more stable and secure employment.

Job insecurity arises when data scientists perceive a lack of stability and assurance about their future within a company. This uncertainty can stem from a variety of factors, including budget cuts, downsizing, or the company’s overall financial health. When employees feel insecure about their job prospects, they may actively start exploring opportunities outside their current organization, jeopardizing the company’s ability to retain top data science talent.

In addition to job insecurity, frequent restructuring can also contribute to data scientists seeking new employment. When a company undergoes significant organizational changes such as mergers, acquisitions, or departmental reshuffling, it often results in a disruption to the existing workflows and team dynamics. Data scientists may find themselves working on entirely different projects, reporting to new managers, or facing unfamiliar work cultures.

This lack of stability and uncertainty can lead to decreased job satisfaction, as employees struggle to adapt to the changes and find their place within the new structure. The stress and anxiety associated with these transitions may ultimately push data scientists to look for opportunities where they feel more confident in their roles and have a clearer path for career growth.

“The constant restructuring and uncertainty about my future at the company became overwhelming. It was hard to focus and be productive when I was always on edge, waiting for the next round of layoffs or reassignments.” – John, former data scientist

For data scientists, job insecurity and frequent restructuring can be major catalysts for seeking new employment. Organizations must recognize the importance of providing a stable and secure work environment to retain their data science talent.

Factors Impact
Job insecurity Higher employee turnover, decreased motivation, reduced productivity
Frequent restructuring Disrupted workflows, unstable team dynamics, decreased job satisfaction

Work Culture Mismatch

Every company has its own unique work culture that encompasses its values, norms, and ways of operating. Sometimes, data scientists may find themselves in a work environment that doesn’t align with their own values or preferred work style. This work culture mismatch can have a significant impact on their job satisfaction and ultimately lead to data scientists deciding to quit their positions.

Data scientists are typically analytical, detail-oriented individuals who thrive in collaborative and innovative work environments. They are motivated by challenging projects, opportunities for learning and growth, and a sense of purpose in their work. However, if the company’s work culture does not support these aspects, data scientists may feel frustrated and unfulfilled in their roles.

For example, a data scientist who values open communication and transparency may struggle in an organization where there is a lack of communication between teams or hierarchical structures that hinder collaboration. Similarly, a data scientist who seeks autonomy and freedom to experiment with new approaches may feel stifled in a company that has rigid processes and a risk-averse culture.

A work culture mismatch can also arise from differences in work-life balance expectations. Data scientists often work long hours and deal with tight deadlines, but they also value having time for personal pursuits and a healthy work-life balance. If a company maintains a culture of expecting constant availability and disregarding work-life balance, data scientists may become burnt out and start looking for opportunities elsewhere.

Ultimately, a work culture mismatch can lead to data scientists feeling disconnected, undervalued, and unappreciated, causing them to seek employment in organizations that better align with their values and work preferences. To retain top talent, companies need to foster a work culture that emphasizes collaboration, autonomy, open communication, professional development, and work-life balance, creating an environment where data scientists can thrive and contribute their best.


In conclusion, there are numerous factors contributing to the trend of data scientists quitting good jobs at great companies. The demanding nature of the work, limited growth opportunities, burnout, lack of challenging projects, inadequate compensation, poor leadership, limited resources, and other reasons can all play a role.

Understanding these factors is crucial for companies to create an environment that fosters the retention of valuable data science talent. By addressing these challenges, companies can attract and retain data scientists, ensuring their long-term success and competitive edge in the industry.

In summary, organizations must prioritize offering a healthy work-life balance, providing opportunities for professional growth, ensuring fair compensation, cultivating strong leadership and positive workplace culture, and investing in the necessary resources and technology. Companies that demonstrate a commitment to these areas are more likely to retain their talented data scientists and maximize their potential.


Why do data scientists quit good jobs at great companies?

Data scientists may quit their seemingly perfect positions at top-notch companies for various reasons. Factors such as the demanding nature of the work, limited career growth opportunities, burnout and work-life balance issues, lack of challenging projects, inadequate compensation and benefits, poor leadership and organizational culture, limited access to resources and technology, insufficient collaboration and team dynamics, the desire for a new challenge, lack of support for professional development, job insecurity and restructuring, and work culture mismatch can all influence their decision to quit.

What are some challenges data scientists face in their work?

Data science roles come with a high level of job demands. The complex and ever-evolving nature of data science work can be mentally and physically exhausting. Data scientists may also experience burnout and struggle to maintain a healthy work-life balance. Additionally, they may face challenges such as a lack of challenging projects, inadequate compensation and benefits, limited resources and technology, poor leadership and organizational culture, insufficient collaboration and team dynamics, and a mismatch in work culture.

What are the factors that influence data scientists’ decision to leave for new opportunities?

Several factors can influence data scientists’ decision to seek new opportunities. These factors include limited career growth opportunities, burnout and work-life balance issues, a lack of challenging projects, inadequate compensation and benefits, poor leadership and organizational culture, limited access to resources and technology, insufficient collaboration and team dynamics, the desire for a new challenge and personal growth, a lack of support for professional development, job insecurity and restructuring, and a mismatch in work culture.

How can companies retain valuable data science talent?

To retain valuable data science talent, companies should create an environment that addresses the challenges and concerns of data scientists. This includes providing opportunities for career growth and advancement, promoting work-life balance and preventing burnout, offering engaging and challenging projects, ensuring fair compensation and benefits, fostering strong leadership and a positive organizational culture, providing access to resources and technology, encouraging collaboration and positive team dynamics, supporting professional development, addressing job insecurity and restructuring concerns, and cultivating a work culture that aligns with the values and preferred work style of data scientists.

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Deepak Vishwakarma


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