Full-Stack Data Scientist (ML MLOps)

Full-Stack Data Scientist (ML + MLOps)

Location: Noida, Uttar Pradesh, India

Experience: 3–6 years

Comp Range: 12 - 18 LPA

About the Company

We are a growing SaaS and consulting technology company headquartered in Noida, building data-driven products and analytic solutions for enterprise customers. Our teams combine product thinking with pragmatic engineering to turn complex business problems into reliable, production-grade systems. We focus on measurable outcomes—reducing operational costs, improving decision accuracy, and automating manual workflows—with an emphasis on maintainability and observability. Small cross-functional teams collaborate closely with product and engineering to ship features quickly while keeping technical quality high. We value curiosity, clear communication, and accountability: every engineer and data scientist owns outcomes and the systems that deliver them. Joining us means working on end-to-end ML systems that move from exploratory analysis to deployed models powering customer-facing services, with strong support for career growth and technical ownership.

About the Role

As a Full-Stack Data Scientist you will own DS/ML components end-to-end: translating product and business needs into analytical tasks, building validated predictive models, and deploying them as reliable services. You will partner closely with product, engineering, and platform teams to design scalable, observable workflows and ensure model correctness and operational readiness. Your work will include exploratory data analysis, robust feature engineering, model evaluation and monitoring, and CI/CD-driven deployments. Success is defined by delivering actionable, production-ready models that improve business metrics and integrate cleanly into existing systems, with clear documentation and measurable impact.

Key Responsibilities

• Translate product and business problems into structured analytical tasks, defining success metrics and delivering clear project scopes that drive measurable business impact.
• Conduct rigorous exploratory data analysis to identify trends, drivers, anomalies, and root causes, and communicate findings to technical and non-technical stakeholders.
• Design, build, and validate predictive models (classification, regression, anomaly detection) with strong emphasis on feature engineering, bias checks, and robust validation strategies.
• Deploy and maintain ML models in production using MLOps best practices: versioning, CI/CD integration, automated testing, monitoring, and retraining pipelines.
• Design and implement ML-backed APIs with attention to throughput, latency, pagination, and fault tolerance to support product requirements.
• Collaborate with data engineering and platform teams to ensure data quality, lineage, and scalable pipelines that support reliable model performance.

Essential Skills & Technologies

• Strong hands-on expertise in Python for data science, including libraries such as pandas, scikit-learn, and model-serving frameworks, with production-grade coding practices.
• Deep practical experience taking work from EDA and insight generation to predictive modeling and operational use, prioritizing impact and correctness.

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  Working knowledge of MLOps practices, including model deployment patterns, CI/CD pipelines, model versioning, monitoring, and retraining approaches.
• Solid understanding of data structures and algorithms relevant to efficient data processing, API systems, and scalable service design.
• Experience developing and integrating with analytics/ML service APIs, and designing for performance and reliability under load.
• Proficient SQL skills for analysis, debugging, and data validation, with familiarity with distributed compute concepts.

Additional Plus

• Exposure to Spark or other distributed data processing frameworks and experience designing solutions for large-scale datasets.
• Experience with cloud ML services (AWS SageMaker, GCP AI Platform, Azure ML) and container orchestration for model serving.
• Familiarity with monitoring, observability, and alerting tools for model and API performance (Prometheus, Grafana, Sentry).

What Youll Bring

• Proven ownership of DS/ML components end-to-end, including system-level design decisions and delivery of production-quality solutions that move business KPIs.
• Strong ability to articulate insights, assumptions, and limitations clearly to both technical and non-technical stakeholders, enabling data-informed decisions.
• A focus on impact, correctness, maintainability, and measurable outcomes, with a desire to continuously improve models and operational practices.