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 leave each year
Multiple team-building off-sites per year
Attend conferences & trainings of your choosing
Compensation & Benefits:
Highly competitive salary
Fully paid medical (HMO, PPO, Kaiser options) and Dental for you, your partner, and your children
Life Insurance and Disability coverage
FieldLevel is seeking a strong analytical mind excited to use the the tools of statistics and data science to help make software people love to use.
As an entry level data scientist you'll get hands on experience using data to build better software. You'll work side-by-side with domain experts, UX designers, and application developers.
The job of the entry level data scientist requires a breadth of knowledge. You should know fundamental methods of statistical analysis. You should have exposure to modern applications of statistics like machine learning, behavioral analytics, and experimental design. You should know SQL, R or Python, and the most common libraries. And because you'll be building software for humans, you should be excited to learn the best ways to gather data both actively and passively as people use our product.
Few entry level candidates are experts in all of these areas, but you should be well versed in some, with a working knowledge of the others and an appetite to gain expertise in several areas.
What you'll do:
Answer tough questions that come up by analyzing our data and provide insights gained from analyzing data.
Design and deploy new datasets to our data catalog to help teams accomplish objectives
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.
Assess and advise on data collection opportunities of new product feature work.
Assess and advise on ML opportunities of new product feature work.
Monitor and analyze outcomes of experiments the team runs and product features the team deploys.
Assist data engineers with pipeline construction
Are humble in the workplace, but you can defend ideas that you believe in.
Strong mathematical problem-solving skills
Some experience with experimental design and data schemas
Sense of when a question can be answered with data and how to do it.
Some 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.
Interested in quickly cycling through computational knowledge building:
explore → hypothesize → test → discover → report
Rigorous, Logical, Skeptical, Proactive, Curious
Education and Experience
BS or higher in data science related field
Online course work in ML or statistical analysis
Past experience with social science research
Loves power analysis, conjoint analysis, ML, data visualization