Data Scientist- P

Data Scientist

Key Responsibilities:

• Lead end-to-end development of

  machine learning

  and

  statistical models

  — from data exploration and feature engineering to training, validation, and deployment.
• Collaborate with cross-functional teams (Product, Engineering, Business, and AI) to identify opportunities for data science solutions.
• Apply advanced techniques in

  predictive modeling, NLP, computer vision, and deep learning

  to solve complex business problems.
• Implement

  Generative AI

  ,

  LLMs

  , and

  LangChain

  frameworks for intelligent automation, content generation, and conversational AI systems.
• Build and optimize

  ETL data pipelines

  using Python, SQL, and cloud services (AWS, GCP, or Azure).
• Perform

  A/B testing

  , model evaluation, and post-deployment monitoring to ensure accuracy and stability.
• Visualize insights and communicate findings to stakeholders using dashboards and storytelling tools (e.g., Power BI, Tableau, Plotly).
• Mentor junior data scientists and contribute to establishing best practices in model development, versioning, and MLOps.

Required Skills & Technologies:

• Programming:

  Python, R, SQL, PySpark

• ML/DL Frameworks:

  Scikit-learn, TensorFlow, PyTorch, Keras

• NLP / GenAI:

  Hugging Face, LangChain, OpenAI APIs, Prompt Engineering

• Cloud Platforms:

  AWS (SageMaker, Bedrock), Azure ML, GCP AI Platform

• Data Tools:

  Pandas, NumPy, Airflow, Kafka, Snowflake, Databricks

• Visualization:

  Power BI, Tableau, Seaborn, Matplotlib, Plotly

• MLOps:

  MLflow, Docker, Kubernetes, Git, CI/CD pipelines
• Strong foundation in

  statistics, probability, linear algebra, and optimization

Preferred Qualifications:

• Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field
• Proven experience leading data-driven projects end-to-end in a production environment
• Experience with

  LLM fine-tuning

  ,

  retrieval-augmented generation (RAG)

  , or

  semantic search

• Excellent communication skills and ability to translate technical findings into actionable business insights

Data Scientist

Key Responsibilities:

• Lead end-to-end development of

  machine learning

  and

  statistical models

  — from data exploration and feature engineering to training, validation, and deployment.
• Collaborate with cross-functional teams (Product, Engineering, Business, and AI) to identify opportunities for data science solutions.
• Apply advanced techniques in

  predictive modeling, NLP, computer vision, and deep learning

  to solve complex business problems.
• Implement

  Generative AI

  ,

  LLMs

  , and

  LangChain

  frameworks for intelligent automation, content generation, and conversational AI systems.
• Build and optimize

  ETL data pipelines

  using Python, SQL, and cloud services (AWS, GCP, or Azure).
• Perform

  A/B testing

  , model evaluation, and post-deployment monitoring to ensure accuracy and stability.
• Visualize insights and communicate findings to stakeholders using dashboards and storytelling tools (e.g., Power BI, Tableau, Plotly).
• Mentor junior data scientists and contribute to establishing best practices in model development, versioning, and MLOps.

Required Skills & Technologies:

• Programming:

  Python, R, SQL, PySpark

• ML/DL Frameworks:

  Scikit-learn, TensorFlow, PyTorch, Keras

• NLP / GenAI:

  Hugging Face, LangChain, OpenAI APIs, Prompt Engineering

• Cloud Platforms:

  AWS (SageMaker, Bedrock), Azure ML, GCP AI Platform

• Data Tools:

  Pandas, NumPy, Airflow, Kafka, Snowflake, Databricks

• Visualization:

  Power BI, Tableau, Seaborn, Matplotlib, Plotly

• MLOps:

  MLflow, Docker, Kubernetes, Git, CI/CD pipelines
• Strong foundation in

  statistics, probability, linear algebra, and optimization

Preferred Qualifications:

• Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field
• Proven experience leading data-driven projects end-to-end in a production environment
• Experience with

  LLM fine-tuning

  ,

  retrieval-augmented generation (RAG)

  , or

  semantic search

• Excellent communication skills and ability to translate technical findings into actionable business insights