Scientist 3, Data Science (Machine Learning Engineer)

Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines , including data ingestion, training, evaluation, deployment, monitoring, and retraining.
• Build and own production-grade ML services that are reliable, scalable, and fault-tolerant.
• Architect and manage async workflows and API-driven systems for ML and data services.
• Integrate ML solutions into complex production environments and distributed systems.
• Design robust systems with a strong focus on failure modes, observability, and guardrails to ensure reliability.
• Develop internal analytical tools used by leadership and cross-functional teams for decision-making.
• Develop interactive internal ML tools and dashboards using Streamlit for model insights, monitoring, and experimentation.
• Experience with cloud platforms (AWS, GCP, Azure).
• Collaborate with data scientists and stakeholders to deliver impactful solutions.
• Required Skills & Qualifications
• Core Engineering Skills
• Strong proficiency in Python , SQL , and building RESTful APIs
• Experience with asynchronous programming and workflows
• Solid understanding of software engineering best practices : Version control ( bitbucket ), Unit and integration testing, Code quality and maintainability
• Machine Learning & MLOps
• Build or integrate data ingestion pipelines (batch or streaming)
• Experience in performing EDA and understand the analysis.
• Proven experience managing the full ML lifecycle .
• Hands-on experience with MLOps practices and tools : Experiment tracking
• Model versioning
• Automated training and deployment pipelines
• CI/CD for ML systems
• Systems, Infrastructure & Orchestration
• Experience building scalable and reliable ML systems in production
• Familiarity with: Containerization (Docker)
• Orchestration platforms (e.g., Kubernetes, Airflow, Prefect, Dagster)
• Infrastructure as Code (IaC)
• Experience with distributed data processing systems (e.g., Spark)
• Understanding of workflow orchestration and scheduling for ML pipelines
• Full Stack Development
• Experience developing end-to-end applications , including: Backend pipelines and services
• Frontend/UI components
• Hands-on experience building internal ML dashboards and tools using Streamlit
• Ability to create intuitive interfaces for monitoring models, exploring data, and enabling stakeholder interaction
• Required Qualifications
• Master's or PhD in Statistics, Data Science, Computer Science, or a related quantitative field.
• 3-4+ years of experience in data science or machine learning pipeline.
• Strong expertise in statistical analysis and machine learning techniques.
• Proficiency in: Python (pandas, numpy, scikit-learn, statsmodels)
• SQL
• Data visualization tools
• Experience working with large-scale operational datasets.

Requirements

• Experience working with Databricks or AzureML.
• Familiarity with big data technologies (Spark, PySpark).
• Experience working with cloud platforms (AWS, Azure, or GCP).
• Knowledge of MLOps practices and model deployment frameworks.
• All your information will be kept confidential according to EEO guidelines.

Additional Information

Role Overview We are looking for a highly skilled Machine Learning Engineer who can design, build, and own end-to-end ML systems in production. This role requires a strong blend of machine learning expertise, backend engineering, and full-stack development, with a focus on building reliable, scalable platforms used by leadership and critical business functions.

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Company Intel

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