Data Scientist II

Job description

**Role Overview** We are looking for a **Senior / Lead Data Scientist** who can own **end‑to‑end data science and machine learning solutions** , from problem formulation to production deployment. This role requires a strong blend of **machine learning expertise, data engineering, MLOps, cloud platforms, and technical leadership** . You will work closely with **product, engineering, and business stakeholders** to design scalable data and ML systems that drive measurable business impact. Your role will include overseeing, supervising and reviewing tasks performed by team members to ensure effective execution of work; managing end-to-end processes and projects for both internal and external clients with responsibility for timely and accurate delivery; issuing clear instructions and directions to team members on tasks to be performed; and mentoring and guiding junior colleagues to Support their skill development, professional growth, and overall success. **Experience** 4+ years **Key Responsibilities** **Data Science & Machine Learning** - Translate business problems into **data science and ML solutions** - Perform advanced **EDA, feature engineering, and model development** - Build and optimize: - Classical ML models (regression, classification, tree‑based models) - Time‑series, anomaly detection, and recommendation systems - Develop and fine‑tune **deep learning models** using PyTorch / TensorFlow Design and evaluate experiments (A/B testing, statistical validation) **GenAI, NLP & LLM Solutions** - Build **NLP and GenAI applications** using modern LLMs - Implement **RAG pipelines** , prompt engineering, and vector search - Integrate LLMs using OpenAI / Azure OpenAI APIs Evaluate model quality, latency, and cost for production LLM systems **Data Engineering & Pipelines (Good to Have)** - Design and build **scalable data pipelines** for batch and streaming use cases - Work with distributed processing frameworks like **Apache Spark** - Orchestrate workflows using **Airflow / Dagster / Prefect/ Azure Data Factory / Databricks** - Handle real‑time data using Kafka or cloud‑native streaming services Ensure data reliability, quality, and performance at scale **MLOps, Deployment & Production** - Own the **full ML lifecycle** : experimentation training deployment monitoring - Implement model versioning, reproducibility, and CI/CD pipelines - Deploy models using REST APIs or batch inference pipelines - Monitor model performance, drift, and data quality in production Work with Docker and Kubernetes for scalable deployments **Cloud & Platform Engineering** - Build solutions on **AWS / Azure / GCP** (at least one in depth) - Work with cloud data platforms like **Databricks, Snowflake, BigQuery** - Optimize system performance and cloud costs Ensure security, access control, and compliance best practices **Architecture, Collaboration & Leadership** - Design **end‑to‑end data and ML architectures** - Make tradeoffs between batch vs streaming, cost vs performance - Mentor junior data scientists and review code and models - Set data science and ML best practices across teams Communicate insights clearly to technical and non‑technical stakeholders **Required Skills & Qualifications** **Core Technical Skills** - Strong proficiency in **Python** and **advanced SQL** - Solid foundation in **statistics, probability, and linear algebra** - Hands‑on experience with **XGBoost, LightGBM** - Experience with **PyTorch or TensorFlow** **Data Engineering (Good to have)** - Strong experience with **Spark / PySpark** - Pipeline orchestration using **Airflow** or similar tools - Experience with relational, NoSQL, and analytical databases - Understanding of data lakes and warehouse architectures **MLOps & DevOps (Optional)** - Experience with **MLflow, DVC, or W&B** - Model deployment using **FastAPI** - Containers and orchestration: **Docker, Kubernetes** - CI/CD and monitoring tools **Cloud Platforms** - Deep expertise in at least one cloud provider: - AWS, Azure, or GCP Experience with managed ML and data services **Preferred / Nice‑to‑Have** - Experience with **LLM frameworks** (LangChain, LlamaIndex) - Vector databases (FAISS, Pinecone, Weaviate) - Streaming frameworks (Flink) - Knowledge of data governance, privacy, and compliance - Experience leading cross‑functional technical initiatives **Machine Learning Algorithms & Techniques (Hands‑On)** **Supervised Learning** - **Linear Models** - Linear Regression - Logistic Regression - Regularization (L1, L2, Elastic Net) - **Tree‑Based Models** - Decision Trees - Random Forest - Gradient Boosting (XGBoost, LightGBM, CatBoost) - **Clustering Techniques** - K‑Means - Hierarchical Clustering - DBSCAN - PCA (feature reduction) - t‑SNE / UMAP (visualization & analysis) **Dimensionality Reduction** **Time Series & Forecasting (Basic–Intermediate)** - Statistical forecasting: - Moving averages - ARIMA / SARIMA (conceptual + basic use) ML‑based forecasting using regression and tree‑based models **Model Evaluation & Optimization** - Cross‑validation techniques - Hyperparameter tuning (Grid Search, Random Search) - Bias–variance tradeoff - Handling class imbalance - Selection of appropriate evaluation metrics Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.