Data Scientist II

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.