Senior Data Scientist
New
F
FliffSports Gaming
Work RemotelyFull-TimeSenior
Salary$135,000 - $160,000
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLData visualizationscikit-learn
Requirements
- 5+ years of experience as a data scientist, marketing analyst, or growth analyst.
- Experience in consumer app, gaming, fintech, or subscription businesses.
- Strong SQL skills for working with large, messy behavioral datasets.
- Hands-on experience building predictive models in Python using libraries like scikit-learn or XGBoost.
- Experience evaluating the long-term and incremental impact of marketing and promotional spend.
- Working knowledge of causal inference methods such as diff-in-diff, synthetic control, CausalImpact, and uplift modeling.
- Solid grounding in marketing measurement concepts: attribution, incrementality, holdouts, cohort analysis, and unit economics.
- Experience with experimentation, including design, sizing, and statistical readout.
- Proficiency with at least one BI/visualization tool like Looker, Tableau, Mode, or Sigma.
- Strong communication skills for presenting complex models and results to diverse stakeholders.
- Bias toward action and shipping iterative, useful solutions.
Responsibilities
- Build and maintain predictive models that drive marketing strategy, including player LTV, churn risk, CAC payback, and propensity-to-convert models.
- Own marketing attribution and incrementality analysis across paid channels (Meta, Google, TikTok, affiliates, influencers, etc.).
- Quantify the causal impact and long-term business value of promotions, bonuses, and lifecycle campaigns.
- Apply causal inference techniques (geo experiments, synthetic control, diff-in-diff, CausalImpact, uplift modeling) to evaluate marketing investments.
- Partner with growth marketers to design, run, and read out experiments including A/B tests and holdout studies.
- Develop and maintain dashboards and self-serve reporting for marketing leaders.
- Clean, structure, and validate data across the marketing stack and collaborate with data engineering to improve data models.
- Translate complex analyses into clear, actionable recommendations for non-technical stakeholders.
- Automate, improve, and scale data usage within the marketing team.
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