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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist II - **Company:** Master Electronics - **Location:** Phoenix, AZ, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), A/B Testing, Artificial Intelligence, Amazon Web Services, Amazon S3, Big Data, Computer Programming, Continuous Integration, Apache Hadoop, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, SQL Databases, Data Processing, Feature Engineering, Pytorch, Apache Spark, Pandas, Scikit Learn, Kubernetes, Information Technology, Power Analysis (Cryptography), Data Analytics, Machine Learning Operations, Optimizely, Stream Processing, Software Version Control, Data Pipelines, Docker, Databricks - **Published:** July 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8c73446a5f5a2ca0 ## About the Role * 3-5 years of professional experience as a data scientist or ML engineer, with a proven record of buildingand 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, sequentialtesting, guardrail metrics, and interpreting results under real-world constraints (novelty eff ects,interference, heterogeneous treatment eff ects) 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 arelated quantitative fi eld, or a Bachelor's degree with 5+ years of equivalent professional experience * Strong programming skills in Python (plus experience in JavaScript), with profi ciency in ML libraries (scikit-learn, PyTorch), data manipulation (pandas, SQL), and statistical analysis * Solid grounding in statistics: hypothesis testing, confi dence 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 andbusiness audiences and to collaborate eff ectively across teams * Demonstrated ability to work independently on complex problems, manage multiple projectssimultaneously, and deliver results in a fast-paced environment Preferred Qualifications * Advanced degree (Master's or PhD) in a relevant fi eld (Statistics, Machine Learning, AI, OperationsResearch, Economics/Econometrics, etc.) * Experience with B2B or ecommerce pricing, such as quote optimization, contract pricing, or price-listmanagement 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-specifi c 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 certifi cations (e.g., Machine Learning Associate/Professional) * Knowledge of model explainability, interpretability techniques, and responsible AI ## Description 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. What you will do?: * 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 ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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