Cracking the Code: Hiring the Right Data Scientist for Your Startup
Most people think of data science as a machine-driven process reserved for those with ample work resources and heavy infrastructure.
But data science isn’t just for the big players. Nor is it only for companies strictly focused on AI projects.
A market set to reach $103 billion by 2027, big data’s rapid growth can be attributed to increased accessibility for smaller organizations and widespread applications in startup environments.
From customer sentiment analysis and service personalization to user profiling and predictive analytics, startups need data science to inform decision-making and sustain competitive advantages.
So, the challenge many startups face isn’t whether to invest in data science or not. It’s how to hire the right person for the job.
Why do startups have such a hard time hiring data scientists?
Although data science is practically industry-agnostic, its applications vary based on company size, product portfolio, and type of customer. Especially when compared to large organizations, startups face unique and multifaceted challenges when approaching data science (and the people behind it).
Startups move quickly.
Being a data scientist at a large corporation means countless 30-minute Zoom calls with successive managers for every proposed change or innovation.
In a startup environment, it’s all about building a product on time. While the enterprise can spend months “reinventing the wheel” from scratch, the startup has to build on existing models.
This means it needs a person who can think outside the box, unearth untapped resources, and come up with innovative solutions faster than its competitors.
High risk, potential reward.
According to VentureBeat, 87% of machine learning models are unsuccessful when deployed in reality. While established companies have both financial and bureaucratic leeway to handle this, its impact on startups is catastrophic.
Early-stage founders need a robust GTM strategy and proof of concept guided by quality data. And they need competent data science professionals who can get them there.
The right combination of skills won’t guarantee success.
Corporate data scientists work in multi-member teams with division of labor and clear responsibilities. An advantage for some and a disadvantage for others, this increases the probability of success but doesn’t guarantee it.
Startup data science teams are often limited to one or two members. And they will be responsible for teaching stakeholders a new way of thinking that guides consequential decisions.
Finding such a person is like finding a needle in a haystack.
Your startup might be your “baby.” But when you’re hiring someone, it likely won’t be theirs.
Big companies with established structure know this, and they can afford to offer corresponding compensation packages and career opportunities to attract the right people.
The data scientist you choose needs to be able to understand your business vision, goals, and culture—and align them with their own values and expectations.
This person is not a mercenary. They need to have an emotional investment in the project. Seeking such a combination of skills requires ample time for research and assessment.
Timing is everything, and founders get FOMO.
Founders are people—and people make decisions based on emotions and learned biases. In plenty of startups we’ve seen, the drive to build out a data science function is rooted in a fear of missing out—i.e., that they’ll fall behind if they aren’t “data-driven” enough.
This notion causes those in charge to rush into hiring data scientists, often at the expense of thoroughness.
When resources and time are limited, startups have to prioritize the challenges that need immediate attention for the business to reach its scaling goals.
Investing too much energy into data science prematurely can hinder progress toward these objectives.
And unless you thoroughly understand the data science needs of your specific business, you’ll hire someone who isn’t well-equipped to handle its unique set of goals and challenges.
Cost, competition, and culture.
The average data scientist makes over $125,000 per year, according to Glassdoor.
The average data scientist makes $126,296 per year, but most tech companies hire at $200,000+. Meta, Google, and Twitter want the best of the best, and so does the new market entrant that just closed a multi-million-dollar Series A.
Within the last five years, an astonishing 98% of data scientists have changed positions at least once. This significant turnover rate is the direct result of a severe shortage of skill proficiency—if these experts want to pursue other opportunities, they can confidently leave their current roles knowing greener pastures are close by.
The most sought-after data scientists know where they fit in and what they’re worth. It’s the startup’s job to make sure the job description, compensation package, and company culture align with their expectations.
The Key to Success: Knowing Your Business
When it comes to hiring data scientists, the key is understanding your business and its needs.
After assessing both internal and external opportunities for data science, you need to strategically attract and evaluate candidates—not just surf the web for resumes.
Follow this step-by-step guide to build a data science team for your startup that can drive lasting success.
1. Evaluate your company: Are you ready to hire a data scientist?
Like we mentioned, FOMO can be tricky. And the constant “go-go-go” mentality most startups have can make it even harder to identify when and if you’re actually ready to take on a data scientist.
To determine your company’s readiness, ask yourself the following questions:
- Do you have enough data? Unless you’re building a data science product or service, your company shouldn’t include a data scientist in its first few hires. Generally, organizations turn to interviews, firsthand customer feedback, and social media for feedback and product ideas until qualitative assessments no longer cut it.
- How much customer insight do you already have? Data scientists provide insight into unknowns and patterns, as well as validate existing hypotheses. But if you’ve already done enough groundwork to understand your customers and develop a product roadmap, you can probably save yourself the cost.
- Do you have adequate foundational infrastructure? Unless you already use a data warehouse (e.g., Snowflake, Databricks), a pipelining tool (e.g., Apache Airflow), and a data visualization platform (e.g., Tableau, Looker) to collect, transform, and visualize your customer data, you shouldn’t bring on a data scientist right away.
- Can you ensure ongoing support? If expectations are not in line and your team doesn’t adopt a data-driven culture, data scientists can become decorative assets instead of having tangible influence on the business—an unfair situation for your new hire.
- Can you justify the ROI of a data scientist? Especially if your startup is pre-revenue, hiring a data scientist might not be a justifiable cost. And if your engineering or product team are well-versed in data analysis and query writing, it’s probably better to hire a consultant or contractor to get the ball rolling.
If, after running through these questions with your team, the role and all its implications (e.g., high salary requirements, a companywide shift to data culture, and providing career growth opportunities) are justifiable, your team is ready to hire and onboard its first data scientist.
2. Define the skills your business needs.
The first hire is the most critical when building your data science team. They will not only shape your company’s data culture but also establish standards for future team members and protocols for hiring and evaluation.
Unfortunately, they (probably) won’t fall into your lap.
So, what makes a great data scientist?
There isn’t a black-and-white answer to that. Company data needs and structure vary, meaning how “great” a data scientist is depends on how well they fit the requirements of the specific organization.
For example, a data scientist at an early-stage startup may be expected to architect and maintain infrastructure, while one in a more mature organization might be focused solely on analytics and insights.
Regardless of the specifics, here are some core competencies you should look for when evaluating potential candidates:
| Data Science Skill | Requirement Type | Skill Description |
| Coding | Hard Skill | Expertise in Python, R, and SQL programming languages to collect, clean, manipulate, and analyze data. |
| Statistics | Hard Skill | Knowledge of statistical concepts and techniques such as hypothesis testing, regression analysis, and clustering to make informed decisions based on data. |
| Machine Learning | Hard Skill | Ability to use algorithms to build models that learn from data and make predictions or classifications on new data. |
| Data Visualization | Hard Skill | Will frequently create data visualizations using tools like Tableau or ggplot, to communicate insights and findings. |
| Communication | Soft Skill | Must convey complex technical ideas to non-technical stakeholders through clear and concise communication. |
| Critical Thinking | Soft Skill | Capacity to analyze data and draw meaningful insights to solve business problems or optimize processes. |
| Project Management | Soft Skill | Manage resources, timelines, and stakeholders to ensure timely delivery of data-driven projects. |
| Business Acumen | Business Impact | Understanding of cross-functional business processes to create impactful data science solutions that improve the product roadmap. |
| Creativity | Business Impact | Capacity to think outside of the box to find unique solutions to complex data-driven challenges. |
| Ethical Considerations | Business Impact | Understanding of ethical considerations in data collection, storage, and analysis, including privacy and bias issues. |
Although the hard skills may seem critical to the role, the skills that truly distinguish the best from the rest are those that aren’t so easily learned.
For your first hire to set the tone for your data culture and potentially shift the organization’s mindset about your product (a must for early-stage startups), they must communicate in a way that non-technical stakeholders can understand.
3. Define the role you’re looking for.
One of the reasons data science roles vary so wildly is that every company utilizes them differently.
In general, there are five different roles we see startups place their data scientists into.
| Role | Description |
| Data Scientist, Product | Works hand-in-hand with engineering and product teams to drive product development and feature design. |
| Data Scientist, GTM | Collaborates with sales and marketing teams to inform customer segmentation, optimize ads, and track campaigns. |
| Data Scientist, Growth | Leverages analytics and experimentation to increase user engagement, retention, and conversion. |
| Data Scientist, Research | Works on complex problems using cutting-edge technologies and advanced algorithms (usually requires a Ph.D.). |
| Data Analyst | Only a data scientist if the job requires them to build complicated models and clearly understand the business. |
There are actually over a dozen roles a data scientist can play in an organization—the above list is by no means exhaustive.
These five roles represent the potential for impact at startups, specifically.
4. Find data scientists with hybrid skill sets.
Most of the hyper-specialized data science roles you see in large companies—actuarial scientist, machine learning expert, etc.—don’t need to exist at startups.
For most startups, we’re firm believers in the hybrid skill set—this could mean having a traditional data scientist who’s well-versed in ETL or a rising engineer with a solid foundation in analytics.
As companies with relatively simple (but varied) functions, startups usually benefit most from generalists—those who understand the mechanics of data science and can also pivot between departments.
How to Attract and Hire the Right Data Scientist
As a skilled, college-educated employee entering a field known for high salaries, the data scientist you’re looking for will need a bit of wooing.
Follow this checklist to attract the right candidates:
1. Develop a strong employer brand.
75% of active job seekers are likelier to apply to a company that proactively manages its employer brand. And most (92%) would consider a role at one with a favorable online perception.
Branding your organization as a great workplace is more than just a logo and tagline. It should capture the company’s values, mission, and culture.
Here are a few considerations your prospective data scientist hires will look for:
- Exciting challenges and problems. Exceptional data scientists take pride in their work—they believe it makes a meaningful impact on the product and company. Publishing articles and white papers with unique research, angles, and insights will communicate innovative, forward-thinking motives that resonate with them.
- Company growth. The first data scientist you hire is a crucial team member who will (hopefully) stay with the company through multiple growth stages. But they’ll want to know that upfront. Use your social media profiles and website to highlight company wins from a personal point of view. And make sure you spotlight your other employees and how they contribute to the bigger picture.
- Culture and values. Your co-founders, company social profiles, and website should all share a cohesive message about your workplace culture. And it should be backed by social proof (e.g., testimonials, employee tweets, congratulatory shoutouts).
2. Simplify your recruitment process.
The majority of the best candidates aren’t looking. They might passively gloss over your company profile or read a press release about your upcoming product launch. But they probably won’t call you up asking for an interview.
So you need to make the process as seamless and attractive as possible. Here’s how:
- Cut the long application process. You don’t need your applicants to write cover letters (which you probably won’t read) and you don’t need them to fill out a dozen questionnaires.
- Keep your outreach concise. In a world of spammy LinkedIn DMs and generic job postings, you—the founder—should be clear and concise. Reach out to prospects with one or two sentences maximum.
- Respond quickly. If an applicant has moved along in the process, give them a response within 24 hours. Considering there’s a smartphone app for just about every communication platform, this should be easy.
- Create a transparent job description. Most job descriptions are sterile (thanks, ChatGPT), guaranteeing prospective applicants won’t have a clue what the job really entails. Explain your product, current challenges, and what the data science role means in your company’s context.
3. Ace the interview.
The interview is your time to assess your candidate’s skills and determine their fit. But evaluating a data scientist isn’t like evaluating a salesperson or marketer.
A few ideas:
- Ask them to assess a current business problem of yours. Pay attention to their follow-up. Do they look at it from a holistic perspective? Do they ask pertinent, detailed questions? How can they articulate their thought process effectively? Is there originality behind their resolution?
- Give them a live coding test. In the same session, ask them to solve a problem by writing a program in Python or R. Pay attention to their problem-solving skills, how they walk you through their coding logic, and how they move past any blocks. Grade them on their ability to parse the logic, not how perfectly they code.
- Dig deep into their experience of one previous project. Don’t just tick boxes on an interview checklist. Ask insightful, open-ended questions about their cross-functional collaboration, how they overcame challenges, and what they would have done differently.
By asking these questions, you can determine whether your candidate is scrappy and resilient enough to work at your startup. You’ll also have a better idea of how they communicate complex data science concepts—a critical skill for someone responsible for a companywide cultural shift.
You and your co-founders must also be prepared to their answer questions honestly. Your best candidates will ask you:
- What the scope and challenges of your business are
- How the data science role fits into the equation
- How exactly you envision them fitting into the company
- The financial health of the company
Be prepared to answer these questions as accurately and honestly as possible. Candidates will appreciate your transparency, and it will help them feel confident if (or when) they commit.
4. Deliver where you can.
Although you might not be able to offer a more competitive salary than bigger companies, there are other attractive benefits you can provide to data science candidates.
For example:
- Flexibility in work hours and location (if possible)
- Travel opportunities
- A clearly articulated path for career growth
- A startup atmosphere that fosters creativity and ownership of projects
- The opportunity to have an outsized impact on the bottom line
- Access to health and wellness perks
- Professional development resources
- Workplace amenities (e.g., standing desks and ergonomic chairs)
Even if there are certain things you can’t offer, focus on what you can. In doing so, you’ll attract more of the right candidates.
Final Thoughts
As you start to build your data science team, it pays to find the right person for the job. Although there isn’t way to guarantee a perfect fit, following the steps outlined above will help you find a candidate that can help your business grow and achieve its goals.
But before you start your search, make sure you have an idea of what success looks like. Sit with your co-founders and evaluate whether or not you have a clear vision (and need) for your data science team.
That way, when it’s time to bring a data scientist on board, you can make the most of their talents.
