Advanced Data Scientist

Minimum Years of Experience:

Key Responsibilities & Required Qualifications

• PhD in Computer Science, Statistics, Applied Mathematics, Data Science, AI, or a related field.
• Proven experience applying ML, deep learning, and statistical techniques to real-world problems.
• Strong programming skills in

  Python

  ,

  R

  , or similar languages, with expertise in

  TensorFlow

  ,

  PyTorch

  ,

  Scikit-learn

  , etc.
• Proficiency in

  SQL

  and experience with large-scale data platforms (Hadoop, Spark, etc.).
• Strong background in mathematics, probability, linear algebra, and optimization.
• Experience in research publication or open-source contribution (preferred).
• Excellent communication skills to explain technical concepts to non-technical stakeholders.

• NLP expertise

  : NER, summarization, Q&A, OCR (Tesseract, Amazon Textract, Azure Form Recognizer).
• Deep understanding of statistical modeling, risk scoring, credit analytics, and fraud detection.
• Experience with vector databases (

  Pinecone

  ,

  Weaviate

  ,

  FAISS

  ,

  Milvus

  ).
• Knowledge of MLOps tools (

  MLflow

  ,

  Kubeflow

  ,

  Weights & Biases

  ).

Must-Have Skills

• Design, build, and experiment with AI/ML models.
• Perform

  EDA

  and feature engineering on large, heterogeneous datasets.
• Validate models using cross-validation, A/B testing, and real-world scenarios.
• Collaborate with product and engineering teams to translate business needs into models.
• Stay updated with the latest research papers, frameworks, and tools.
• Proven experience in designing and implementing AI/ML models.
• Data analysis and feature engineering on diverse datasets.
• Familiarity with model validation techniques (cross-validation, A/B testing).

• Data Analysis:

  Extract insights from sales datasets to identify revenue drivers and forecast trends.

• Model Development:

  Build predictive models (e.g., customer segmentation, lead scoring) using Python and scikit-learn.

• Algorithm Implementation:

  Apply ML algorithms (e.g., churn prediction, opportunity scoring) with TensorFlow and PyTorch.

• Develop Processes:

  Establish best practices and workflows for model development and deployment.

• Collaboration:

  Partner with cross-functional teams to gather requirements and deliver accurate models.

• Education:

  Bachelors or master’s degree in computer science, Data Science, or a related field.