Overview

Remote, 7+ years experience, IST

Key Responsibilities

• Advanced Analytics and Modeling:
  • Develop and implement sophisticated statistical and machine learning algorithms including regression, classification, recommendation, natural language processing and computer vision models.
  • Work with lead data scientists and technical leads to assist in developing custom deep learning models using architectures like RNNs, CNNs, transformers.
  • Analyze large datasets to extract meaningful insights through descriptive statistics, interactive charts using Plotly, High Charts.
  • Strong command in Python programming, functional design.
  • Advanced SQL knowledge and ability to write optimized queries for larger datasets.
  • Develop and host APIs on GCP / Azure / AWS for models produced and evaluate and track performance over time. Familiarity with CI/CD pipelines will be preferred.
  • Extract and integrate data from multiple relational and NoSQL data sources.
• Business Insights and Strategy:
  • Collaborate with cross-functional teams to understand business requirements. Familiarity with media planning and buying references and metrics will be preferred.
• Project Support:
  • Collaborate, Assist and deliver on given tasks in end-to-end data science projects, from problem formulation to model deployment.
  • Manage project timelines, resources, and deliverables.
  • Collaborate with stakeholders across the organization to drive data initiatives with technical teams, business analysts to build data initiatives.

Qualifications

• Education: Bachelor’s, Master’s or Ph.D. in a quantitative field (e.g., Data Science, AI, Computer Science, Statistics, Mathematics, or related).
• Experience: Minimum of 3+ years in data science, machine learning, or related roles.

Technical Skills

• Proficiency in Python, or similar programming languages and efficient API development.
• Hands on experience with SQL based warehouses. Preferably Snowflake or equivalent.
• Strong knowledge of machine learning techniques (e.g., regression, classification, clustering, DNN).
• Experience with big data tools (e.g., Spark) and cloud platforms (e.g., AWS, GCP, Azure).
• Experience with Snowflake related data engineering a good to have.