Were looking for a Data Scientist to turn complex data into actionable insights that drive product, operations, and business decisions.
You will own the full analytics and modeling lifecycle-from problem framing and data wrangling to experimentation, deployment, optimization and communicating your output to the customer.
Responsibilities
Partner with product, engineering, and business teams to define problems and success metrics
Explore, clean, and transform large, messy datasets from multiple sources
Build and validate statistical and machine learning models (e.g., classification, regression, time series, NLP, recommendation, anomaly detection)
Translate findings into clear narratives, dashboards, and recommendations that influence decisions
Productize models and monitor performance
Establish data quality checks and model governance (bias and drift monitoring)
Document methods, assumptions, and results; present to technical and non-technical audiences.
You will own the full analytics and modeling lifecycle-from problem framing and data wrangling to experimentation, deployment, optimization and communicating your output to the customer.
Responsibilities
Partner with product, engineering, and business teams to define problems and success metrics
Explore, clean, and transform large, messy datasets from multiple sources
Build and validate statistical and machine learning models (e.g., classification, regression, time series, NLP, recommendation, anomaly detection)
Translate findings into clear narratives, dashboards, and recommendations that influence decisions
Productize models and monitor performance
Establish data quality checks and model governance (bias and drift monitoring)
Document methods, assumptions, and results; present to technical and non-technical audiences.
Requirements:
3+ years in data science, analytics, or applied ML
Proficiency in Python (pandas, numpy, scikit-learn)
Advanced SQL proficiency with a proven track record of building performant, maintainable queries (joins, aggregations, reshaping, window functions).
Strong statistics foundation: hypothesis testing, sampling, regression
Experience with ML workflow: feature engineering, model selection, validation, and evaluation
Ability to communicate complex ideas simply, with strong data storytelling skills.
3+ years in data science, analytics, or applied ML
Proficiency in Python (pandas, numpy, scikit-learn)
Advanced SQL proficiency with a proven track record of building performant, maintainable queries (joins, aggregations, reshaping, window functions).
Strong statistics foundation: hypothesis testing, sampling, regression
Experience with ML workflow: feature engineering, model selection, validation, and evaluation
Ability to communicate complex ideas simply, with strong data storytelling skills.
This position is open to all candidates.
