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