As a Data Scientist working with Awardco's Go-to-Market (GTM) teams, you'll have the opportunity to partner with stakeholders across Marketing, Sales, Revenue Operations, and Customer Success, helping influence Awardco's customer acquisition and retention strategy. You'll dig into customer behavior, uncover churn and retention insights, and design experiments that reveal what actually drives retention and expansion. You'll also build models that flag risk and opportunity early and explore open-ended questions that shape how we think about customer success.
This is a high-ownership role at the intersection of causal inference, predictive modeling, and business strategy, where you'll bring structure to ambiguous problems and work in a modern, fast-paced data environment, turning data into insights that help the business grow.
What you will do:- Act as a thought partner to GTM stakeholders throughout each stage of the initiative lifecycle - from identifying new opportunities, to measuring the effectiveness of initiatives, to contributing to final shipping decisions
- Design, run, and analyze experiments (e.g., A/B tests) and other causal-inference methods to measure the true impact of GTM initiatives
- Explore open-ended questions and build measurement frameworks to identify the drivers of key business metrics, size the opportunity to improve them, and turn ambiguous patterns into clear hypotheses worth testing.
- Build, validate, and maintain predictive models such as customer health/churn scores, expansion likelihood, predicted LTV, and lead scoring
- Partner with our BI and Data Engineering teams on metric definitions and data infrastructure
- Work primarily in Python and SQL against Snowflake, our cloud data warehouse, helping build reliable analytics and ML infrastructure
What you will bring:- Bachelor's or Master's degree in Statistics, Computer Science, Economics, Data Science, or another quantitative field
- 3-5 years of applied data science experience
- Strong Python proficiency and experience with common data science libraries (e.g. pandas, scikit-learn, statsmodels)
- Familiarity with designing and analyzing A/B tests, with working knowledge of causal inference methods (e.g., difference-in-differences, synthetic control, propensity scoring)
- Familiarity with data visualization libraries (e.g. matplotlib, seaborn, plotly) and tools (e.g. Streamlit, Tableau, Looker)
- Experience building and deploying machine learning models
- SQL fluency and hands-on experience with a cloud data warehouse (we use Snowflake)
- A track record of partnering with non-technical stakeholders as a strategic advisor
- Comfort scoping moderately ambiguous problems with guidance, rather than needing a fully specified roadmap
- Strong exploratory/ad hoc analytical instincts - comfortable digging into open-ended questions without a pre-defined method
What will make you stand out: - Experience in B2B SaaS, customer success, or go-to-market analytics
- Experience working with modern ML Ops tools, such as Snowflake or Databricks ML features (e.g. Snowflake feature store, model registry and inference services)
- Experience working with orchestration tools such as Airflow or Orchestra
- Experience on a small or early-stage data science team
