What are the responsibilities and job description for the Data Scientist position at Scale.jobs?
About The Role
This role focuses on designing, validating, and scaling statistical models and machine learning systems that drive automated decision-making across core product lines. The work sits at the intersection of causal inference, predictive modeling, and product analytics, turning unstructured user interactions and transactional data into actionable algorithms.
The data scientist will partner closely with engineering and product management teams to build robust experiment frameworks and design ML pipelines that run efficiently in production. The role requires balancing rigorous mathematical methodology with pragmatic engineering trade-offs to deliver measurable business impact.
Key Responsibilities
This role focuses on designing, validating, and scaling statistical models and machine learning systems that drive automated decision-making across core product lines. The work sits at the intersection of causal inference, predictive modeling, and product analytics, turning unstructured user interactions and transactional data into actionable algorithms.
The data scientist will partner closely with engineering and product management teams to build robust experiment frameworks and design ML pipelines that run efficiently in production. The role requires balancing rigorous mathematical methodology with pragmatic engineering trade-offs to deliver measurable business impact.
Key Responsibilities
- Formulate, train, and validate predictive and prescriptive models using PyTorch, LightGBM, or scikit-learn to solve complex user behavior challenges
- Design and implement end-to-end A/B testing frameworks, including power analysis, multi-armed bandits, and post-hoc segmentation to measure product feature impact
- Build reproducible data pipelines and feature engineering workflows in SQL and PySpark to process multi-terabyte datasets
- Deploy statistical models as scalable APIs or microservices in cloud environments, utilizing Docker and Kubernetes
- Define, track, and model key product and business metrics, translating complex analytical findings into strategic recommendations for technical leadership
- Establish rigorous model monitoring frameworks to track prediction drift, performance degradation, and data quality issues in production
- 3–6 years of professional experience as a Data Scientist or quantitative researcher, with a track record of deploying models that directly impact product metrics
- Strong proficiency in Python and SQL, with deep experience in data science libraries such as pandas, NumPy, scikit-learn, and Statsmodels
- Solid theoretical foundation in probability, statistics, causal inference, and experimental design
- Experience working with cloud data warehouses and distributed computing platforms such as Snowflake, Databricks, Spark, or AWS
- Master’s or PhD in Statistics, Computer Science, Operations Research, Economics, or a highly quantitative field
- Bonus: Experience with Bayesian modeling, deep learning architectures, or implementing MLOps tools like MLflow or Prefect