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Master Electronics
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Data Scientist II

Master Electronics · Phoenix, AZ, US

Actively hiring Posted 4 months ago

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.

Basic qualifications

  • 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.

Preferred qualifications

  • 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.

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