Associate Data Scientist

• Support building and maintaining data ingestion and transformation workflows (ETL/ELT) for marketing and media datasets
• Assist with implementing data validations and QA checks (schema checks, null checks, duplication checks, count reconciliation) to ensure datasets are reliable and analytics‑ready
• Help monitor pipeline runs and refresh schedules, investigate failures using logs, and escalate issues with clear context and evidence
• Support development of reusable, governed datasets / data products that are consistent and easy for BI and analytics teams to consume
• Contribute to documentation (data flow notes, runbooks, assumptions, source mapping) to improve operational stability and handoffs
• Collaborate with cross‑functional partners (MSO BI, MSO Ops, Analytics teams, Vendors) to ensure inputs and outputs are aligned and dependable
• Participate in continuous learning and improvement to strengthen engineering fundamentals, reliability practices, and delivery discipline
• Analyze and understand the functional & non-functional requirements for Investment and Media Analytics function, and translate them into prototype, technical specifications.

BASIC QUALIFICATIONS

• Bachelor’s (3 to 6 Yrs) or Master’s (2 to 4 Yrs) degree in Computer Science, Engineering, Information Systems, Data Science, or a related quantitative/technical field
• Experience in data engineering, software engineering, analytics engineering, or data/tech role
• Familiarity with data concepts (tables, schemas, joins, basic transformations)
• Proficiency in SQL and data analysis and structured data
• Strong problem‑solving and analytical reasoning skills
• Effective written and verbal communication skills
• Strong learning mindset and willingness to work with new tools, datasets, and business contexts

Preferred Qualifications

• Exposure to Python programming for automation
• Exposure to cloud/data tools such as Databricks, Spark/PySpark, Azure/AWS, ADF/Airflow
• Experience with ETL/ELT pipelines, batch processing, or orchestration concepts
• Familiarity with data quality checks, profiling, data governance, logging, monitoring, or incident triage practices
  • Exposure to DevSecOps practice