Senior Data Scientist - AI Labs

Job Requirements

Senior Data Scientist

Data & Analytics

Primary Responsibilities

• Lead Generative AI projects within cross-functional teams, driving innovation and business impact.
• Develop and deploy predictive models using Python or R and libraries such as pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, or Keras.
• Apply advanced machine learning algorithms and statistical modeling techniques to solve business problems.
• Work extensively with Natural Language Processing (NLP) frameworks like NLTK, SpaCy, Gensim, and transformer models (e.g., BERT, GPT).
• Implement text analytics techniques including topic modeling, sentiment analysis, and text classification.
• Utilize SQL and NoSQL databases for data extraction and management.
• Create compelling data visualizations using tools like Matplotlib, Seaborn, or Tableau.
  

• Contribute to multiple data analysis projects, with a strong focus on text-based data.
• Build, validate, and deploy predictive models tailored to unstructured text datasets.
• Handle large-scale text data, including cleaning, preprocessing, and feature engineering.
• Collaborate with BI analysts and engineers to prototype and present data science solutions.
  

• Bachelor’s Degreein:
• Computer Science
• Statistics
• Mathematics
• Engineering
• Data Science
• Postgraduate Degree(preferred) in:
• Data Science
• Machine Learning
• Artificial Intelligence
• Applied Statistics
  

Experience

• 5-8years of relevant experiencein data science, with a strong focus on NLP, machine learning, and model deployment.
  

• Proficiency in Python or R and associated data science libraries.
• Strong understanding of NLP techniques and transformer-based models.
• Experience with big data tools and handling large unstructured datasets.
• Ability to translate business problems into data science solutions.
• Excellent communication and presentation skills.
• Strong debugging and problem-solving capabilities.
• Experience working in collaborative, agile environments.
  

• Timely and error-free delivery of data science projects.
• Effective deployment and performance of predictive models.
• Resolution of infrastructure and model training issues.
• Technical leadership and innovation in Gen AI and NLP initiatives.