Data Scientist

Title: Data Scientist

Location: Remote

Salary up to 16 LPA

Experience: 2 to 5 Years

Job & Division Summary

We are looking for a technically strong Data Scientist – MLOps & Analytics Governance with 4–5 years of experience who will own the full MLOps lifecycle, enforce data quality governance and insights validations. The ideal candidate is highly proficient in writing optimised, scalable Python and SQL code with deep hands-on experience running large-scale workloads in BigQuery on GCP. This role is critical to ensuring ML models are deployed reliably in cloud and that all insights reaching stakeholders are statistically sound and validated. The candidate is expected to actively leverage Generative AI tools (such as GitHub Copilot, Claude, or equivalent LLM-based assistants) to accelerate software development, automate repetitive coding tasks, and improve overall engineering productivity. Domain exposure to manufacturing, IoT analytics, or rotating equipment such as air compressors is a strong advantage.

Key Responsibilities

• Own the end-to-end MLOps lifecycle – model packaging, versioning, cloud deployment, monitoring, and automated retraining pipelines on GCP using Vertex AI, MLflow, or Kubeflow.
• Design and maintain CI/CD pipelines for ML models, ensuring reliable, repeatable deployments with full model registry traceability from training data through to production artifacts.
• Define and enforce data quality governance standards across all ML feature pipelines and training datasets – including schema contracts, null checks, range validation, and detection of training-serving skew.
• Validate model outputs and analytical findings for statistical soundness and insights validation – reviewing for data leakage, biased evaluations, distributional assumptions, and reproducibility before results reach stakeholders.
• Set up model monitoring to track prediction drift, data drift, and performance degradation in production, and trigger automated retraining workflows when thresholds are breached.
• Work with large-scale IoT sensor datasets from industrial equipment such as air compressors and rotating machinery to build scalable, production-grade time-series and fault-detection pipelines.
• Collaborate with data engineers, domain experts, and product managers to translate requirements into scalable data science solutions, and clearly communicate model performance and business impact to technical and non-technical stakeholders. Actively use Gen AI coding assistants to accelerate development, generate boilerplate, write unit tests, and review code quality.

Mandatory Skills

• Hands-on experience in data science, ML engineering, or applied AI roles with strong focus on production systems.
• Deep ownership of MLOps – CI/CD for ML, model versioning, deployment automation, drift monitoring, and retraining pipelines on GCP (Vertex AI) or AWS (SageMaker).
• Advanced proficiency – writing and reviewing optimised, cost-efficient SQL including partitioning, clustering, query plan analysis, and scalable transformation design for large-scale workloads.
• Strong Python skills for writing and reviewing production-grade ML code – feature engineering, batch scoring, and inference pipelines using scikit-learn, TensorFlow, PyTorch, or Pandas. Proficient in using Gen AI coding assistants (GitHub Copilot, Claude, or similar) to boost development velocity and code quality.
• Hands-on experience implementing data quality governance – schema contracts, automated profiling, pipeline-level validation, lineage tracking, and quality scorecards integrated into ML workflows.
• Proven ability to perform insights validation – identifying data leakage, biased model evaluations, distributional shifts, and statistically unsound conclusions prior to stakeholder delivery.
• Strong grounding in statistical modeling – regression, classification, time-series forecasting, hypothesis testing, and model behaviour under distributional shift.
• Familiarity with IoT data architectures – streaming pipelines, time-series databases (InfluxDB, TimescaleDB), and high-frequency sensor data processing at scale.
• Experience with version control (Git), code review workflows, and working in agile cross-functional teams alongside data engineers and product managers.

Desired Skills

• Domain knowledge in air compressor systems, rotating equipment, or industrial machinery – understanding of operational parameters such as vibration, pressure, temperature, and flow rates.
• Exposure to predictive maintenance frameworks and condition-based monitoring in a manufacturing or heavy-industry environment.
• Experience with dbt or similar frameworks for scalable, tested, and documented SQL transformations in BigQuery.
• Familiarity with industrial IoT protocols such as MQTT and OPC-UA, and cloud IoT ingestion services on GCP or AWS.
• Hands-on experience with Generative AI tools for software development – using LLM-based coding assistants (GitHub Copilot, Claude, Cursor, or equivalent) for code generation, automated test writing, SQL optimisation, and documentation; ability to critically review AI-generated code for correctness, security, and performance before merging into production pipelines.

Basic Qualifications

• B.Tech / M.Tech – Computer Science or Data Science or Artificial Intelligence or Electrical / Mechanical Engineering.
• Certifications in MLOps or cloud ML (Google Professional ML Engineer, AWS ML Specialty) are a plus.