Senior Data Scientist

Role Overview

Senior Data Scientist / ML Engineer

Key Responsibilities

• Design, develop, and optimize

  machine learning and deep learning models

  for large-scale datasets.
• Build and manage

  ETL/data pipelines

  handling terabyte-scale data.
• Deploy models to production using

  cloud platforms (AWS, Azure, GCP)

  and ensure scalability, reliability, and monitoring through

  MLOps best practices

  .
• Implement APIs and services (FastAPI/Flask) to integrate ML solutions into business applications.
  
• Work on

  NLP or ML Engineering use cases

  , including transformer-based models (BERT, GPT) or distributed ML pipelines (Spark, PySpark).
• Perform

  time-series forecasting, anomaly detection, and model evaluation/optimization

  .
• Collaborate with business stakeholders to understand KPIs, align solutions with business strategy, and present insights effectively.
  
• Deliver projects from

  proof-of-concept to production deployment

  , ensuring measurable business impact.

Mandatory Skills

• Programming & Engineering:

  Advanced Python (OOP, API development, clean code, testing, documentation), SQL, ETL/data pipeline design.

• Machine Learning:

  Strong grasp of classical ML algorithms, experience with deep learning (TensorFlow, PyTorch, Keras), time-series and anomaly detection.

• Cloud & Deployment:

  Hands-on with AWS/Azure/GCP ML services, containerization (Docker, Kubernetes), MLOps (versioning, monitoring, retraining).

• NLP / ML Engineering:

  Experience with transformer models (BERT, GPT, NER, sentiment analysis) OR scalable ML pipelines and distributed computing.

• Consulting Skills:

  Strong communication, client engagement, and ability to translate technical solutions into business outcomes.

Nice-to-Have Skills

• Generative AI & LLMs:

  LangChain, RAG, prompt engineering, fine-tuning.

• Computer Vision:

  OCR, Document AI, CNNs, YOLO.

• Recommendation Systems:

  Collaborative, content-based, hybrid.

• Advanced Analytics:

  Causal inference, A/B testing, experimental design.

• Big Data Tools:

  PySpark, Dask.

• Visualization:

  Tableau, Power BI, or Python libraries (Plotly, Dash).

• Databases:

  NoSQL, Vector DBs.

• DevOps/MLOps:

  CI/CD workflows and version control.