In the ever-evolving landscape of data science, professionals often encounter a unique set of challenges that can lead to a mid-career crisis. This phenomenon, characterized by feelings of stagnation, disillusionment, or a desire for change, can strike even the most dedicated data scientists and analysts. As the industry continues to advance at a breakneck pace, keeping up with the latest technologies, methodologies, and best practices can become overwhelming, contributing to the sense of being left behind.
To delve into “Mid-Career Crisis in data science” we have with us Gary Patel who stands as the Senior Director of BI & Analytics at Albertsons Companies, bringing over 15 years of robust experience in data, analytics, and machine learning across tech, ecommerce, and banking sectors with giants like Sony Playstation, Dell, and eBay. His deep expertise in business intelligence, analytics, and enterprise product management has driven significant stakeholder and customer success. Gary is known for his ability to craft strategic analytics roadmaps, develop scalable data platforms, and lead high-performing teams of up to 35 people. An ex-startup founder with a global work background in the US, Australia, London, and India, his skill set extends to advanced analytics techniques, including recommendation engines, customer segmentation, and machine learning, using tools like SQL, Python, and Tableau.
The discussion is not limited to identifying the signs of a mid-career crisis but extends to actionable strategies for overcoming it. From embracing continuous learning and mentorship to considering alternative career paths within the data science ecosystem, the discussion aims to empower professionals with the tools and mindset needed to thrive beyond the crisis.
As we navigate through Gary Patel’s experiences and insights, it becomes evident that a mid-career crisis, while daunting, can be a pivotal point for growth, self-discovery, and renewed passion in one’s career. Whether you’re an individual contributor deep in the technical trenches or a manager overseeing strategic initiatives, this article offers perspectives on how to manage and move beyond the mid-career slump, turning challenges into opportunities for advancement and fulfillment in the dynamic field of data science.
AIM: Starting our discussion on mid-crisis management in data science careers, can you explain what constitutes a mid-career crisis and its implications for professionals, particularly in the context of rapid technological advancements and the pressure to stay relevant?
“The symptoms, which are universal across roles, industries, and job types, include demotivation and disengagement, even when engaged in work that may seem impactful to others.”
Gary Patel: Very timely, as some of these thoughts, I must admit, have occurred to me as well, with all the emerging technologies and Gen-AI becoming a prevalent topic, accompanied by extensive media coverage. This might lead one to feel as if they’re missing out, or not keeping pace with trends, which is a common sentiment. The insights I plan to share, while not limited to data science or data analysis, are applicable across various career paths.
Considering a typical career span of approximately 50 years, give or take, we observe that a mid-career crisis tends to occur between the ages of 35 to 45. Analyzing job satisfaction over time reveals a U-shaped curve, indicating that satisfaction generally increases post mid-career crisis, supported by extensive research. Let’s delve into the nature of this career crisis.
The symptoms, which are universal across roles, industries, and job types, include demotivation and disengagement, even when engaged in work that may seem impactful to others. Despite external recognition and accolades, an individual may not feel the same level of job satisfaction experienced early in their career.
Various factors contribute to these feelings, which we will explore further in our discussion. Symptoms include a loss of passion for one’s job, harboring resentment towards colleagues, leadership, and company decisions, and a general lack of enthusiasm for the job.
Moreover, external factors such as job loss due to inadequate skills, both technical and soft, can exacerbate the crisis. The difficulty in securing a new position often stems from a skillset that does not align with the demands of the desired role, among other reasons. This encapsulates the essence of a mid-career crisis.
AIM: In the realm of data science, how crucial is it for professionals, especially those in managerial roles, to stay technologically adept and prevent feeling outdated? Could you share your personal strategies or practices to maintain relevance in the ever-evolving tech landscape?
“Professionally, to navigate through a mid-career crisis, adopting a learning mindset is essential.”
Gary Patel: Holding a managerial position doesn’t exempt one from being knowledgeable about current technologies and emerging tools. Various factors can precipitate a mid-career crisis. You might find yourself using outdated tools or realizing that your team isn’t employing cutting-edge technologies for several reasons, including potential automation of your job. For instance, tasks that once required extensive coding can now be completed with a few lines of code due to advances in programming languages like Python.
It’s critical to recognize that personal issues may also contribute to these professional challenges. While technological changes can be demotivating, it’s worthwhile to consider underlying personal factors that might be influencing these feelings.
Professionally, to navigate through a mid-career crisis, adopting a learning mindset is essential. For individuals with significant industry tenure, it’s imperative to embrace change, which includes learning new approaches and unlearning obsolete practices.
As a leader, one may not engage in hands-on coding regularly; however, it’s still possible to remain technically engaged. For example, setting up a home server for personal coding projects can provide immense satisfaction and keep skills sharp. Engaging in passion projects can also be a proactive way to stay connected with the evolving landscape.
In project management, it’s helpful to distinguish between teleological and atelic activities. Teleological activities, deriving from the Greek word ‘telos’ meaning ‘end,’ have definitive goals. Teleological activities have specific endpoints, such as completing a sales pitch to secure a deal or creating a framework to boost customer retention. In contrast, atelic activities, like understanding customer behavior, offer continuous learning and relationship building, which can be inherently fulfilling and contribute to long-term success.
Lastly, seeking mentors and coaches for guidance is invaluable. These relationships provide a sounding board for ideas and can offer fresh perspectives, especially helpful when navigating through challenging periods.
AIM: How can mid-level data scientists recognize early symptoms of a potential mid-career crisis, and is it possible to either avoid it entirely or mitigate its impacts? What strategies can be employed to manage or alleviate the negative outcomes associated with such a crisis?
“A recommended method for monitoring these changes is to maintain a journal. Documenting your feelings can help in identifying a trend from positivity to negativity, suggesting that you might be entering a phase of career crisis.”
Gary Patel: As previously discussed, recognition of a mid-career crisis often begins with personal introspection. You may notice a diminished sense of proficiency in your role, accompanied by feelings of disengagement and a lack of motivation. This could persist despite working on familiar projects within the same organization, leading to a decrease in job satisfaction.
You might find yourself feeling irritable at work, during interactions with colleagues or even in discussions with your manager, sensing a shift in your workplace demeanor. A recommended method for monitoring these changes is to maintain a journal. Documenting your feelings can help in identifying a trend from positivity to negativity, suggesting that you might be entering a phase of a career crisis. These are intrinsic indicators that should not be overlooked.
Externally, the crisis might manifest through significant professional setbacks, such as job loss or being assigned to less impactful projects due to not meeting the necessary skill requirements. Additionally, when seeking new opportunities, the challenge of not being selected due to a perceived deficit in skills can be a clear sign of a larger career trend that needs to be addressed.
AIM: How can data science professionals navigate the choice between individual contributor and management tracks during a mid-career crisis to stay relevant and engaged? Can you offer insights or examples to help those feeling stagnant in their careers?
“Exploring other opportunities with the guidance of a mentor can be enlightening.”
Gary Patel: There are primarily two career paths, the individual contributor track and the management track. Contrary to some traditional views, managers are not inherently more important or impactful than individual contributors. Both can have a significant impact through their expertise. Choosing the right path should be based on personal preference, skills, and where one feels they can excel.
For instance, a senior data scientist might reach a point where they feel they’ve plateaued. Exploring other opportunities with the guidance of a mentor can be enlightening. An innovative idea might involve leading a machine learning educational initiative within the company or sharing expertise through teaching at academic institutions or conferences. These alternative avenues can provide renewed motivation and satisfaction.
The tech stream, for example, includes roles such as data engineering, data science, machine learning operations, and machine learning engineering. Then there’s the business side, appealing to those more interested in the commercial aspects of technology. It’s important to note that not everyone seeks a customer-facing position; many thrive on the technical development side, working on solutions that empower the business.
Within the data field, one can aspire to become a senior solution architect, a senior data scientist, or a senior AI specialist. These roles involve advising executive leadership and contributing to the company’s technical strategy. Conversely, the business track offers roles in marketing, operations, finance, etc., where business analytics plays a pivotal role even without building machine learning models or AI algorithms.
The emergence of roles like product managers for data platforms and data products reflects the trend of data being treated as a product itself. These roles are ideal for individuals who possess a blend of technical know-how and a customer-focused mindset.
Finally, the role of a program manager serves as a bridge between technical and business teams. Firms such as Amazon and Microsoft employ technical program managers who oversee projects without necessarily having coding expertise but with a deep understanding of both technological and business requirements. This comprehensive approach is crucial in coordinating and leading complex initiatives.
AIM: What strategies do you employ to stay current with the rapid advancements in the data science industry?
“A foundational step for any professional in the tech sector is to actively follow industry news. Publications such as Analytics India Magazine is a valuable resource for the latest updates and insights.”
Gary Patel: As suggested, a foundational step for any professional in the tech sector is to actively follow industry news. A publication such as Analytics India Magazine is a valuable resource for the latest updates and insights. Additionally, engaging in AI-focused conferences provides a platform to connect with peers and discuss the latest applications and trends in artificial intelligence. Such events are fertile ground for encountering innovative products and ideas.
Recently, I encountered a company at a conference making remarkable strides in AI-driven search technology. It’s these types of discoveries that underscore the value of participating in industry forums. Smaller-scale meetups offer a more intimate setting for in-depth discussions about leveraging AI and the tools being used to innovate within the field.
Seeking out these opportunities is crucial and entirely feasible, with numerous organizations facilitating such engagements. For instance, engaging with Data Science and AI professionals on platforms like LinkedIn can yield insights into current discussions and allow you to observe and join conversations with other thought leaders in the space.
Dedicating time to expand your network and absorb the wealth of available information is essential. When it comes to enhancing your skills, an abundance of resources and courses are tailored to fit various roles, whether technical or business-oriented. There is indeed something for every professional looking to upskill in this ever-evolving landscape.
AIM: Can you share a personal anecdote about encountering a mid-career crisis, how you navigated through it, and your final advice to our audience on managing such phases?
“I spent time contemplating my core values, passions, and professional aspirations, often visualizing them as a Venn diagram to identify where they intersected with viable career opportunities.”
Gary Patel: Reflecting on the year 2019, which marked 15 years into my career, I realized it was a pivotal time. My journey began as a data scientist, and I had progressed to a role where I partnered closely with business stakeholders in marketing and product development. However, two years into this role, I encountered a period where I felt my growth had stagnated. I was no longer learning, which prompted a period of self-reflection to understand my discontent.
During this time, I faced both personal and professional transitions, including welcoming a new child into my family. This significant life event led me to take paternity leave, which proved to be an invaluable period for gaining perspective. I spent time contemplating my core values, passions, and professional aspirations, often visualizing them as a Venn diagram to identify where they intersected with viable career opportunities.
Upon returning from leave, I had a candid discussion with my employer and transitioned into a new role as a product manager for data platforms. This was a departure from my previous experiences, but my user-centric, customer-focused approach made me confident in my ability to succeed. The change was indeed rewarding; it offered a refreshing environment surrounded by product managers and owners, which aligned well with my business-oriented mindset.
The role was not only enjoyable but also became a cornerstone of my career. I’m proud of the data platforms I developed and the skills I honed during that time. This experience granted me profound insights into my professional identity and aspirations, shaping the trajectory of my future endeavors.
