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About the Role

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.

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

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
• Hands-on Databricks experience for building and deploying data science workloads at scale
• 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, 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

Skills

• Pricing optimization
• Experimentation (A/B testing)
• Causal analysis
• Dynamic pricing
• Price elasticity estimation
• Demand forecasting
• A/B testing
• Multivariate testing
• Causal inference techniques
• Uplift modeling
• Difference-in-differences
• Synthetic controls
• Instrumental variables
• Machine Learning
• MLOps
• Databricks
• MLflow
• CI/CD pipelines
• Version control
• Python
• JavaScript
• scikit-learn
• PyTorch
• pandas
• SQL
• Statistics
• Hypothesis testing
• Confidence intervals
• Regression
• Bayesian methods
• AWS
• S3
• Redshift
• SageMaker
• Spark
• Docker
• Kubernetes
• Model explainability
• Model interpretability
• Responsible AI

Location

• Phoenix, AZ

Work Type

• Full-time

Experience Level

• 3-5 years of professional experience

Education Level

• Master's degree in Computer Science, Statistics, Mathematics, Engineering, Operations Research, or a related quantitative field
• Bachelor's degree with 5+ years of equivalent professional experience
• Advanced degree (Master's or PhD) in a relevant field (Statistics, Machine Learning, AI, Operations Research, Economics/Econometrics, etc.)

Benefits

• World-class and affordable insurance plans
• 401(k) match program
• Tuition assistance
• Employee Assistance Program (EAP)
• Perspectives
• Healthcare Advocate
• Working Advantage Discount Program
• Paid holidays
• PTO accrual
• Floating Holiday
• Supportive personal and parental leave policies
• Company-sponsored donation match 3 for 1
• Volunteer Time Off (VTO)
• Employee Resource Groups
• Company-funded and voluntary AD&D Life Insurance

About the Company

• Master Electronics is a leading global authorized distributor of electronic components, family-owned for over 50 years.
• We thrive in a fast-paced, entrepreneurial environment where flexibility, professionalism, and a self-starter mindset are essential.
• Our success is built on strong relationships, responsive service, and genuine added value.
• We are committed to building a workplace where everyone feels respected, supported, and empowered to succeed.

Equal Opportunity

• Master Electronics is committed to providing equal employment opportunities for all applicants and employees.
• We do not unlawfully discriminate based on race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, veteran status, marital status, creed, or any other protected characteristic.
• We provide reasonable accommodations in compliance with the ADA and other applicable laws, and we strictly prohibit harassment of any kind.
• This commitment applies to every part of our workplace—from recruitment and hiring to promotions, training, compensation, benefits, and even company events.