Data Scientist II

Master Electronics•Phoenix, AZ

23h•Onsite

About The Position

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.

Requirements

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, MLfl ow), AWS (S3,Redshift, SageMaker), 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

Nice To Haves

Advanced degree (Master's or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.)
Experience with B2B or ecommerce 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, GrowthBook)and metric frameworks
Exposure to industry-specific domains such as ecommerce, marketing analytics, risk/fraud, supply chain, or logistics
Fluency with big data frameworks (Spark, Hadoop), streaming systems, and container/orchestration tools(Docker, Kubernetes)
Databricks certifications (e.g., Machine Learning Associate/Professional)
Knowledge of model explainability, interpretability techniques, and responsible AI

Responsibilities

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