Senior Data Scientist

1 day ago

Bengaluru Gurugram Hyderabad, India Kiya Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Job Description Senior Data Scientist / Machine Learning Engineer

Role Overview

We are seeking a highly skilled and client-focused Senior Data Scientist / ML Engineer with strong expertise in Python, machine learning, and cloud deployment. The ideal candidate will combine deep technical knowledge with the ability to engage stakeholders, translate insights into business outcomes, and deliver scalable AI/ML solutions end-to-end.

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.

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