Data Scientist

We’re hiring a Data Scientist to turn data into decisions. You’ll design experiments, build predictive models, and partner with product, engineering, and leadership to solve high-
impact business problems — from forecasting demand to personalizing user experiences. This role spans the full lifecycle: problem framing, data exploration, modeling, and
communicating results that drive action.
Key Responsibilities:
Analysis & Modeling (50%):
Design, build, and validate statistical and machine learning models (classification, regression, clustering, forecasting, etc.)
Explore and analyze large, messy datasets to identify patterns and opportunities
Translate ambiguous business questions into well-defined, testable hypotheses
Build and evaluate recommendation systems, churn models, pricing models, or similar (per business need)
Experimentation (20%):
Design and analyze A/B tests and multivariate experiments
Define success metrics and guardrail metrics for product experiments
Ensure statistical rigor (power analysis, significance testing, avoiding p-hacking)
Production & Engineering Collaboration (15%)
Partner with ML/data engineers to deploy models into production pipelines
Monitor deployed models for drift, degradation, and data quality issues
Write production-quality, version-controlled, reproducible code
Communication & Strategy (15%):
Present findings and recommendations to cross-functional stakeholders and leadership
Build dashboards and self-serve tools for non-technical teams
Influence product and business strategy with data-backed insight
Required Qualifications:
Master’s or PhD in Statistics, Computer Science, Mathematics, Economics, or related quantitative field (or equivalent experience)
2–5+ years of professional data science experience
Strong Python skills (pandas, NumPy, scikit-learn, statsmodels) or equivalent R expertise
Solid foundation in statistics and probability (hypothesis testing, regression, experimental design)
Advanced SQL skills; comfort with large-scale data warehouses
Experience designing and analyzing A/B tests
Strong written and verbal communication skills — able to explain technical concepts to non-technical audiences
Preferred / Nice-to-Have
Experience with deep learning frameworks (PyTorch, TensorFlow) for applicable use cases
Experience with cloud-based ML platforms (SageMaker, Vertex AI, Databricks)
Experience deploying models into production (APIs, batch pipelines)
Knowledge of causal inference methods
Domain expertise in [your industry — fintech, healthtech, e-commerce, etc.]
Experience with big data tools (Spark, BigQuery, Snowflake)

Work arrangement
Hybrid