Data Scientist

Data Scientist – Machine Learning / GenAI

Job Title: Data Scientist – Machine Learning & AI Location: Remote / Hybrid – USA Duration: 12+ Months Contract Experience: 5-10 Years

Job Summary

We are seeking an experienced Data Scientist with strong hands-on experience in machine learning, statistical modeling, neural networks, and predictive analytics , along with some practical experience working with Generative AI and Large Language Models (LLMs) . The candidate will work with software engineering, and business teams to develop, evaluate, and deploy data-driven machine learning solutions.

Required Skills & Experience

• 5+ years of experience in Data Science, Machine Learning, or Applied AI .
• Strong programming experience with Python and commonly used data science libraries.
• Hands-on experience developing and deploying machine learning models for classification, regression, clustering, forecasting, or recommendation use cases.
• Strong understanding of supervised and unsupervised learning , feature engineering, model selection, validation, and performance evaluation.
• Practical experience with neural networks and deep learning .
• Experience with frameworks such as PyTorch, TensorFlow, or Keras .
• Understanding of neural network architectures including CNNs, RNNs/LSTMs, Transformers , or similar architectures.
• Experience working with NLP, text analytics, or unstructured data is highly desirable.
• Some hands-on experience with Generative AI / LLMs , including prompt engineering, embeddings, vector search, RAG, or LLM-based applications.
• Familiarity with OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI , or comparable GenAI platforms is a plus.
• Experience with SQL and working with structured and unstructured datasets.
• Experience using Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn , or equivalent tools.
• Understanding of model evaluation, hyperparameter tuning, cross-validation, and model explainability .
• Experience with cloud platforms such as AWS, Azure, or GCP .
• Familiarity with ML pipelines, MLOps, model deployment, and monitoring .
• Ability to translate business problems into measurable data science and machine learning solutions .

Key Responsibilities

• Analyze large and complex datasets to identify patterns, trends, and business opportunities.
• Develop and optimize machine learning models for business and operational use cases.
• Design and implement neural-network and deep-learning solutions where appropriate.
• Perform feature engineering, data preprocessing, model training, and validation.
• Evaluate models using appropriate statistical and machine-learning metrics.
• Experiment with different algorithms and architectures to improve model performance.
• Work with data engineers to prepare reliable datasets and scalable data pipelines.
• Develop proof-of-concepts using LLMs and Generative AI for selected business use cases.
• Build or contribute to RAG, embeddings, vector-search, and prompt-engineering solutions.
• Collaborate with engineering teams to productionize machine-learning models.
• Monitor model performance and recommend improvements or retraining strategies.
• Present analytical findings, model results, and recommendations to technical and business stakeholders.

Preferred Qualifications

• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field .
• Experience with Transformers, LLMs, NLP, RAG, embeddings, or vector databases .
• Experience with Databricks, Spark, Snowflake, AWS, Azure, or GCP .
• Familiarity with Docker, Kubernetes, MLflow, Airflow, or CI/CD .
• Experience taking machine-learning models from prototype to production .
• Experience working in enterprise environments such as financial services, retail, healthcare, technology, or manufacturing .

Technical Stack

Languages: Python, SQL Machine Learning: Scikit-learn, XGBoost, LightGBM Deep Learning: PyTorch, TensorFlow, Keras AI/GenAI: LLMs, Prompt Engineering, RAG, Embeddings, Vector Search, Transformers Data: Pandas, NumPy, Spark, Databricks, Snowflake Cloud: AWS / Azure / GCP MLOps: MLflow, Docker, Kubernetes, CI/CD Visualization: Power BI, Tableau, Matplotlib, Seaborn

Core requirement: The candidate should be genuinely hands-on with traditional machine learning and neural networks , with practical exposure to GenAI/LLMs

Work Location: Remote