Senior Data Scientist

Role Summary

We are seeking a Staff AI/ML Engineer & Data Scientist with deep expertise in traditional machine learning, Deep learning and strong MLOps experience to lead the design, deployment, and maintenance of production-grade ML systems. You will architect robust ML pipelines, apply advanced statistical techniques, and ensure models are accurate, explainable, and scalable. While the primary focus will be on traditional supervised, unsupervised, and time-series modeling, light experience with retrieval-augmented generation (RAG) is a plus. The individual needs to have devops experience for setting up Databases, CI/CD (Databricks end-to-end experience is plus).

Most Important Skills / Responsibilities

• Traditional ML Expertise – Apply regression, tree-based models, SVMs, clustering, and forecasting; feature engineering and hyperparameter tuning (including anomaly prediction).
• End-to-End Model Development – Full lifecycle from preprocessing, feature engineering, training, validation, deployment, and monitoring.
• Statistical Analysis – Hypothesis testing, Bayesian methods, and interpretability techniques.
• DevOps / MLOps Experience – Database setup, Databricks, AWS,Azure, CI/CD, VectorDBs, GraphDB.
• Masters or PhD is mandatory.
• Site Visits – Required intermittently to Normal, IL for initial scope understanding.
• Domain Knowledge – Manufacturing, sensors, PLC data (prior experience is a plus).

Key Responsibilities

• Define ML architecture, best practices, and performance standards for enterprise-scale solutions.
• Lead end-to-end ML lifecycle (preprocessing → training → deployment → monitoring).
• Build scalable ML pipelines & APIs (Python primary, Golang for backend services).
• Implement CI/CD pipelines for ML (automated retraining, versioning, monitoring, rollback).
• Apply advanced statistical analysis methods.
• Collaborate cross-functionally with engineering, analytics, and product teams.
• DevOps support: Databases, Databricks, AWS,Azure, VectorDBs, GraphDB.

Must Have

• 8+ years in applied ML/Data Science (3+ years in senior/staff-level role).
• Master’s degree or PhD (mandatory).
• Expert Python for ML (Golang preferred for backend integration).
• Proven deployment of traditional ML models into production with measurable impact.
• Strong knowledge of ML frameworks (Scikit-learn, XGBoost, LightGBM) and libraries (Pandas, NumPy, Statsmodels).
• Hands-on MLOps with MLflow (preferred), Databricks (preferred), Kubeflow, Vertex AI Pipelines, AWS SageMaker Pipelines.
• Experience with model monitoring, drift detection, and automated retraining.
• Strong database expertise (SQL, NoSQL).

Preferred

• Exposure to retrieval-augmented generation (RAG) pipelines & vector databases.
• Time-series analysis & anomaly detection.
• Cloud deployment expertise (AWS, Azure, GCP).
• Familiarity with distributed frameworks (Spark, Ray).