Scientist 3, Data Science (Machine Learning Engineer)

Job Description

As a highly skilled Machine Learning Engineer at SanDisk, you will be responsible for designing, building, and owning end-to-end ML systems in production. Your role will require a strong blend of machine learning expertise, backend engineering, and full-stack development to build reliable, scalable platforms used by leadership and critical business functions.- 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.- Collaborate with data scientists and stakeholders to deliver impactful solutions. Required Skills & Qualifications:- 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.Qualifications:- Masters or PhD in Statistics, Data Science, Computer Science, or a related quantitative field.- 34 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.Preferred Qualifications:- 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.You will be joining SanDisk, a leading global provider of flash memory and solid-state storage solutions, known for designing and manufacturing products such as SSDs, memory cards, and USB flash drives for consumer, mobile, and enterprise applications. Founded in 1988, the company has been a pioneer in flash technology and remains one of the worlds largest suppliers of NAND flash memory. As a highly skilled Machine Learning Engineer at SanDisk, you will be responsible for designing, building, and owning end-to-end ML systems in production. Your role will require a strong blend of machine learning expertise, backend engineering, and full-stack development to build reliable, scalable platforms used by leadership and critical business functions.- 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.- Collaborate with data scientists and stakeholders to deliver impactful solutions. Required Skills & Qualifications:- Strong proficiency i

Keyskills :
PythonSQLRESTful APIsMachine LearningDockerKubernetesAirflowSparkData VisualizationMLOpsPrefectDagster