The Academic in Industry: A Cultural and Pragmatic Shift

When I started the transition from over 10 years as a cognitive psychologist in academia to industry as a data scientist, I initially believed securing a job offer and preparing for the career move would be the biggest hurdles. I didn't give much thought to what would happen after. My academic training already equipped me with key industry-relevant skills: advanced data analysis (ML, statistics), experimentation, end-to-end project management, and strong mentoring and presentation skills. However, after this transition, I often felt like a foreigner in a new culture. While I am capable of my responsibilities, the execution of work and feedback have sometimes differed from my expectations.

So, why did I feel this way?

This article shares my personal journey as I reflect on that transition after four years of working as a data scientist. For academics, your experience will be unique, but I hope my learnings offer some guidance. For employers and managers, I hope this knowledge will offer insights into suitable roles for academics, the reasons behind their behaviors, and effective strategies for guiding their transition.

The Shift: From Scientific Accuracy to Business Efficiency

One of the biggest learnings from my transition is that in industry, value is primarily determined by business efficiency rather than scientific accuracy. Business efficiency balances the cost of your effort with the business impact — maximizing the influence of your analytical outputs while minimizing the time and effort spent (similar to the concept of ROI for a workstream). It's about working effectively and efficiently to achieve business impact, from fulfilling stakeholder requests, discovering insights, to informing product launch decisions. Therefore, business efficiency guides your task prioritization, alignment with team and organizational goals, project selection, and time allocation.

This is very different from academic training. Academic training prioritizes robust and accurate answers through years of thorough research, with efficiency being secondary. In academia, inefficiency is an acceptable tradeoff for improving accuracy – spending years to improve results by a small margin or expanding one aspect of a theory can be considered a great success. Furthermore, even with extensive research, academics are trained to cautiously draw conclusions and explicitly acknowledge uncertainties, often highlighting the wide range of unknowns and unaccounted confounding factors that challenge their own conclusions.

So, academic training is the opposite of business efficiency in this regard. Academic training aims to use as much effort as possible to make conclusions that are as cautious as possible. On the other hand, business needs require using as little effort as possible to make conclusions that are as certain as possible.

The Culture Shock: Reconciling Academic Training with Business Realities

When I started in industry, I felt uncomfortable presenting results from limited analyses, narrow testing, or small, time-bound datasets. Surprisingly, stakeholders and managers were consistently satisfied, often describing the results as thorough, sufficient, and remarkably detailed – a contrast to my own internal assessment of the limitations.

This difference extended to how I drew and communicated conclusions. My academic training encouraged presenting findings with complex conditional statements and acknowledged uncertainties. However, in the business world, such complexity tends to confuse people; communication prioritizes potential impact and actionable next steps, not the nuances of scientific methods or the analysis's limitations.

This cultural gap also showed in my work habits. I often poured significant effort into conducting numerous analyses, striving for exhaustive thoroughness in my projects. I spent extra hours validating results across datasets and perspectives. While this dedication earned stakeholder trust and team appreciation, something still felt misaligned. I often felt I was doing extra work to satisfy my own academic conscience. For the business, this additional effort often seemed unnecessary.

These persistent feelings of confusion highlighted the importance of business efficiency. If extra analysis, exhaustive exploration, or complex conditional explanations wouldn't alter business decisions or stakeholder actions, then that extra work was unnecessary. My key learning was understanding where academic rigor intersected with, and sometimes diverged from, the need for efficient and actionable insights. Ultimately, companies hire academics not just for their analytical skills, but also for their business interpretation and opinions informed by their analysis.

Implications for Employers: Leveraging the Academic Mindset

The following insights also highlight potential mismatches between certain roles and academics' unique strengths that stem from their training.

Autonomy and Vision: Academia fosters strong autonomy and ownership. Individuals who thrive in research often excel in roles with significant independence, defining approaches and shaping project vision. Their strong problem-solving ability and minimal need for instruction translate to self-driven employees comfortable owning complex initiatives. However, this also means they may need more time to adjust to positions with rigid regulatory constraints or conventions that limit independent or unconventional solutions.

Navigating Ambiguity: Academic training helps develop skills to address inherently vague research questions. For example, exploring the latest ML techniques for product integration involves research, understanding utility, and determining business application – a wayfinding process from ideation to realization similar to how academics conduct research. Their training enables them to effectively dissect complex problems into smaller, logical questions, providing a structured approach to ambiguity. However, academics may need guidance on framing objectives around tangible business impact and stakeholder interests.

Logical Reasoning and Process Mapping: Finally, the emphasis on logical reasoning in academic training allows individuals to rapidly map processes and identify logical gaps, often leading to understanding business challenges through upstream and downstream investigation to establish a causal flow. Therefore, when the big picture view is unclear, they may over-investigate. Learning to balance this deep dive with operating under some logical uncertainty is something academics may need guidance on.

One of my past projects highlights the benefits of efficiently applying academic training to solve business problems. A leading job board company was facing declining employer satisfaction due to fewer applications for older job postings. Knowing the full context of products and business, I narrowed my analysis to the impact of job age ("posted X days ago" tag) versus job visibility (ranking system for older jobs) on job performance. By breaking down the business questions into structured and rigorous analyses, the data revealed that while both job visibility and job age statistically significantly impacted job performance, job age's impact magnitude on revenue and applications was far greater, even when controlling for various factors. Academic research habits were utilized to tackle the open-ended question from the right angles. In this case, the approach’s robustness and thoroughness increased stakeholder trust, providing product managers with clear direction, expectations, and estimations for decisions.

Connecting Worlds

To bridge gaps and integrate academics effectively requires close collaboration: 

• Begin by explicitly discussing business efficiency: collaboratively define a project's desired impact and align the necessary rigor with timelines and practical outcomes. 
• Be on alert for academics getting stuck in a "rabbit hole" – excessively focusing on a narrow point with extensive analysis for a single issue without clear ROI. 
• Using examples from past projects can be invaluable. Past projects show academics what level of analysis proved effective, how findings were presented, and how they successfully convinced and supported different stakeholders’ needs. 
• Focus on communication by creating a data-driven business story with tailored decision guidance for different functions within the organization, moving beyond simply presenting objective evidence.

Over time, guide them in developing their own calibration system to determine the necessary depth of analysis for each project and to understand the specific needs of stakeholders across different functions. This understanding will empower them to tailor their analysis, results, and presentations to meet those diverse needs and effectively address the expectations of teams like products, marketing, growth, and the C-suite.

By fostering this collaborative environment and guiding academics to navigate the nuances of industry, companies can unlock their unique strengths, leading to impactful contributions and the full realization of their tremendous potential.

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