Data Scientist II

To be a family that uses our collective superpowers to do significant good.

Master Electronics has an exciting career opportunity for a Data Scientist
.

As a Data Scientist
, you’ll be a key contributor in designing, building, and evaluating data-driven decision systems, with a strong emphasis on pricing optimization, experimentation (A/B testing), and causal analysis that directly influence product and business outcomes.

• Design, build, and refine pricing and optimization models, including dynamic pricing, price elasticity estimation, margin optimization, and demand forecasting, that directly drive revenue and profitability decisions
• Own the experimentation lifecycle: design and run A/B and multivariate tests, define success metrics and guardrails, determine sample sizes and test duration, analyze results with statistical rigor, and communicate causal impact to stakeholders
• Apply causal inference techniques (uplift modeling, difference-in-differences, synthetic controls, instrumental variables) where randomized experiments aren’t feasible
• Translate business problems into ML solutions; build models for prediction, classification, or recommendation; implement feature engineering, model training, hyperparameter tuning, evaluation, and deployment
• Develop scalable data pipelines on Databricks; integrate experimentation and ML systems with modern data and MLOps platforms (Databricks, MLflow); establish CI/CD pipelines, version control, testing, and monitoring to ensure model quality and reliability
• Partner with software engineers, data engineers, product managers, and subject-matter experts; present insights and recommendations to technical and non-technical stakeholders; translate complex analyses into clear narratives
• Research and apply emerging ML techniques; contribute to improving team standards and mentoring junior team members

• 3-5 years of professional experience as a data scientist or ML engineer, with a proven record of building and deploying ML models in production
• Hands‑on experience with pricing, revenue, or marketing optimization, such as price elasticity modeling, dynamic pricing, promotion optimization, or mathematical optimization methods
• Demonstrated expertise in A/B testing and experimentation: hypothesis design, power analysis, sequential testing, guardrail metrics, and interpreting results under real-world constraints (novelty effects, interference, heterogeneous treatment effects)
• Hands‑on Databricks experience for building and deploying data science workloads at scale
• Master’s degree in Computer Science, Statistics, Mathematics, Engineering, Operations Research, or a related quantitative field, or a Bachelor’s degree with 5+ years of equivalent professional experience
• Strong programming skills in Python (plus experience in JavaScript), with proficiency in ML libraries (scikit‑learn, PyTorch), data manipulation (pandas, SQL), and statistical analysis
• Solid grounding in statistics: hypothesis testing, confidence intervals, regression, and Bayesian methods
• Knowledge of MLOps tools and cloud platforms, especially Databricks (Spark, MLflow), AWS (S3, Redshift, Sage Maker), or similar services
• Excellent communication skills; ability to explain complex technical concepts to both technical and business audiences and to collaborate effectively across teams
• Demonstrated ability to work independently on complex problems, manage multiple projects simultaneously, and deliver results in a fast-paced environment
• Preferred Qualifications
• Advanced degree (Master’s or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.)
• Experience with B2B or e‑commerce pricing, such as quote optimization, contract pricing, or price‑list management in a distribution or catalog business
• Familiarity with experimentation platforms (in‑house or commercial, e.g., Optimizely, Statsig, Growth Book) and metric frameworks
• Exposure to industry‑specific domains such as e‑commerce, marketing analytics, risk/fraud, supply chain, or logistics
• Fluency with big data frameworks (Spark, Hadoop), streaming systems, and container/orchestration tools…