Scientist 3, Data Science (Machine Learning Engineer)

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.

Key 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)