Senior Data Scientist

• Translate complex business challenges into scalable data products and machine learning models
• Own projects from exploratory analysis through production deployment
• Write complex SQL and engineer features
• Deploy baseline models quickly and iterate toward more complex solutions
• Manage the complete model lifecycle, including data extraction, feature engineering, deployment, A/B testing, and performance monitoring
• Partner with Engineering, Product, Business, and Finance stakeholders to define technical requirements and translate model outputs into actionable insights
• Write clean, modular, production-ready code
• Participate in peer code reviews and contribute to technical best practices
• Break down ambiguous, complex requirements into executable milestones
• Provide technical guidance to junior data scientists and analysts

Requirements

• 5+ years of applied experience in Data Science or ML Engineering
• Track record of delivering production-ready models that drive measurable business value
• Strong expertise in Python and SQL
• Deep familiarity with pandas, NumPy, scikit-learn, and standard ML frameworks such as PyTorch, XGBoost, or LightGBM
• Experience working in cloud-based environments such as AWS or GCP
• Familiarity with CI/CD workflows, Git, and MLOps principles
• Ability to connect technical metrics to business outcomes and select appropriate algorithms
• Ability to explain technical concepts, model limitations, and analytical findings to non-technical stakeholders
• BS, MS, or equivalent experience in a quantitative field
• Preferred: experience taking initial machine learning models from exploratory notebooks to automated production pipelines
• Preferred: experience in customer behavior and propensity modeling, forecasting, optimization, or causal inference

Core Competencies

Demonstrates expertise in Data Science and Machine Learning Engineering, with a strong focus on developing production-ready models that deliver measurable business value. Proficient in Python, SQL, and various ML frameworks, with the ability to translate complex data challenges into actionable insights for stakeholders.

Highest-signal resume keywords

• Python Programming
• SQL Proficiency
• Machine Learning Model Development
• Cloud-Based Environments (AWS, GCP)
• Data Lifecycle Management

Hard Skills

• Data Science
• Machine Learning Engineering
• Feature Engineering
• A/B Testing
• Performance Monitoring
• Model Deployment
• Exploratory Data Analysis
• Technical Documentation
• Algorithm Selection
• Statistical Analysis

Soft Skills

• Technical Communication
• Collaboration
• Mentoring
• Problem-Solving
• Adaptability

Industry Keywords

• Data Products
• Business Insights
• Customer Behavior Modeling
• Forecasting
• Optimization
• Causal Inference

Tools & Technologies

• Pandas
• NumPy
• Scikit-Learn
• PyTorch
• XGBoost
• LightGBM
• Git
• CI/CD Workflows
• MLOps Principles
• Cloud Platforms

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