LandOffer
Data Scientist
Job details
- Experience
- 3+ years
- Education
- Master's degree
- Posted
- Oct 23, 2026
- Last confirmed open
- Oct 23, 2026
About this role
About The Role
The role is for a hands-on data scientist who turns messy, real-world data into models that ship to production and drive measurable business decisions - not dashboards that sit unread. You will work with ML engineers and data engineers across the full modeling lifecycle: problem framing, experimentation, statistical validation, and deployment, in a stack built on Python, SQL, Spark, and modern cloud tooling.
Key Responsibilities
Frame ambiguous business problems as testable ML and statistical questions, and communicate tradeoffs to technical and non-technical stakeholders Design and run controlled experiments (A/B tests, quasi-experimental designs), computing sample sizes, guardrail metrics, and causal estimates with rigor Build, train, and validate predictive models - gradient boosting, logistic regression, time-series forecasting, and NLP classifiers - using scikit-learn, XGBoost, and PyTorch Develop feature pipelines in Python and PySpark, partnering with data engineers to ensure reliability from raw data through training and inference Partner with ML engineers to productionize models, including feature stores, batch/online scoring, and monitoring for drift and performance decay Communicate results through clear analyses, technical write-ups, and visualizations using SQL, notebooks, and BI tooling (Looker or Tableau) What We Are Looking For 3–6 years of experience in data science or applied machine learning, with at least one model taken from prototype to production Expert-level SQL and strong Python for data manipulation and modeling (pandas, NumPy, scikit-learn) Hands-on experience with A/B testing and causal inference techniques; you can explain when an uplift model or difference-in-differences is the right tool Experience processing large datasets with Spark, Databricks, or equivalent distributed tooling Master's degree in statistics, computer science, economics, or a related quantitative field (or equivalent practical experience) Strong written and verbal communication - able to defend methodology and simplify findings for executive audiences Bonus: experience with dbt, Airflow, MLflow, or experimentation platforms (e.g., in-house A/B tooling); exposure to LLM-based analytics or retrieval systems