Data Scientist II

Master Electronics•Phoenix, AZ

74d•Onsite

About The Position

Master Electronics has an exciting career opportunity for a Data Scientist II. As a Data Scientist, you’ll be a key contributor in designing, building, and evaluating data-driven decision systems, with a strong emphasis on experimentation, causal analysis, and AI/LLM-powered applications that directly influence product and business outcomes. This mid-level position sits between our senior scientists and junior analysts, ideal for someone with experience in owning experiments end-to-end, shipping decision-support systems, and productionizing LLM and agent-based workflows.

Requirements

3-5 years of professional experience as a data scientist or ML engineer; proven record of building and deploying ML models in production.
Master’s degree in computer science, Statistics, Mathematics, Engineering, Operations Research or a related quantitative field or 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.
Hands-on experience with large language models (LLMs), generative AI, agentic systems, or AI-based product features.
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.

Nice To Haves

Advanced degree (master’s or PhD) in a relevant field (Statistics, Machine Learning, AI, etc.).
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).
Knowledge of model explainability, interpretability techniques and responsible AI.

Responsibilities

Translate business problems into ML solutions; build models for prediction, classification or recommendation; implement feature engineering, model training, hyperparameter tuning, evaluation and deployment.
Design and productionize AI and generative-AI solutions, including prompt engineering, retrieval-augmented generation, and multi-agent orchestration.
Create intelligent agents or tool-using workflows that automate decisions and enhance customer experiences.
Develop scalable data pipelines; Integrate experimentation and AI systems with modern data and MLOps platforms (e.g., 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 AI/ML techniques (generative AI, deep learning, agentic systems); contribute to improving team standards and mentoring junior team members.