FieldLevel is the athletic recruiting network. Helping athletes find the right teams and coaches the best talent for their rosters.  FieldLevel goes beyond helping organize and streamline the recruitment process. Our network introduces a new level of trust into recruiting by allowing coaches to advocate for their athletes directly.  The result?  Talent finds the right team. Each year over 18,000 high school athletes commit to college programs through FieldLevel. We play a critical role in the lives of our coaches and athletes, supporting them on their journey to play at the next level. At the end of the day, we succeed when our members succeed. At FieldLevel a diverse, inclusive, and equitable workplace is one where all employees, whatever their gender, race, ethnicity, national origin, age, sexual orientation or identity, education or disability, feels valued and respected. We are committed to a nondiscriminatory approach and provide equal opportunity for employment and advancement for all employees.  We respect and value diverse life experiences and heritages and ensure that all voices are valued and heard. What Work-life Balance Means to FieldLevel:Flexible work schedule Work from home when needed (This position will begin remotely) Generous vacation policy 10 Paid holidays and the days between Christmas and new years 1 week of sick each year Multiple team-building off-sites per year Attend conferences & training of your choosingCompensation & Perks:Highly competitive salary Fully paid medical (HMO, PPO, Kaiser options) and Dental for you, your partner, and your children  Life Insurance and Disability coverage 401K Matching Stock options


United States

Data Analyst Interested in Social Networks, ML and User Experience

FieldLevel is seeking a strong analytical mind excited to use the tools of statistics and data science to help make software people love to use.

As a product data analyst, you'll sit on a cross-functional team with a mission to build out amazing product features. You'll work side-by-side with a domain expert, UX designers, and application developers. As a group, you'll be empowered to diagnose, experiment, and transform application features as you see fit.

The job of the product data analyst is demanding in the breadth of knowledge it requires. You should be proficient with fundamental methods of statistical analysis. Plus, you should have experience with modern applications of statistics like machine learning, behavioral analytics, and experimental design. You should know SQL, R or Python, and the most common libraries. You will be on a team that creates software for humans, so you should be opinionated about the best ways to gather data both actively and passively as people use our product.

Few people are experts in all of these areas, but you should be an expert in some, with a working knowledge of the others and an appetite to gain expertise in several areas.

What you'll do:

  1. Keep focused on how the team can learn:
    1. from the data we have
    2. and by collecting new data.
  2. Answer tough questions that come up by analyzing our data and provide insights gained from analyzing data.
  3. Align the data collection efforts of your team with the data collection efforts of the rest of the company.
  4. Manage analytical escalation: help team members learn how to answer simple questions themselves, provide your own analysis to answer more difficult queries, and collaborate on the hardest questions with the rest of the data science team.
  5. Assess and advise on data collection opportunities of new product feature work.
  6. Assess and advise on ML opportunities of new product feature work.
  7. Monitor and analyze outcomes of experiments the team runs and product features the team deploys.


  • Are humble in the workplace, but you can defend ideas that you believe in.
  • Strong mathematical problem-solving skills particularly as applied to product development.
  • Experienced with experimental design and good data schema construction
  • Good sense of when a question can be answered with data and how to do it.
  • Experience with ML methods
  • Good at explaining general and in particular with plots and numbers.
  • Can speak clearly and simply when discussing analytical topics (doesn't intimidate people with complexity)
  • Proficient with data science programming languages like Python, R, SQL, Regex, Scala.
  • Proficient with data science software platforms like Jupyter, Tableau, Power BI, Spark.
  • Strong sense of the applications of statistics to business operations
  • Interested in quickly cycling through computational knowledge building at a daily interval:
    • explore → hypothesize → test → discover → report


Rigorous, Logical, Skeptical, Proactive, Curious

Education and Experience

  1. BS or higher
  2. Online course work in ML or statistical analysis
  3. Previous position as a data analyst/scientist or researcher

Bonus if

  • Past experience with social science research
  • Loves power analysis, conjoint analysis, ML, data visualization