Data Analyst - AI & ML

We are looking for a detail-oriented Data Analyst to join our core AI/ML team. The ideal candidate will work closely with the AI/ML Lead, business teams, and other verticals to prepare high-quality datasets, generate insights, build basic statistical/heuristic models, and support ongoing ML initiatives. This role is perfect for someone who enjoys combining strong analytical skills with hands-on data work and business understanding.

• Collect, clean, prepare, and structure data for ML training and analytical use cases.

• Perform exploratory data analysis (EDA) to identify trends, patterns, outliers, and data issues.

• Build and maintain dashboards, KPIs, and reports for product and business teams.

• Work with the ML Lead to generate feature datasets, validate data assumptions, and evaluate model outputs.

• Build basic heuristic rules, logistic regression models, and preliminary statistical models when required.

• Conduct data validations, labeling assistance, and sampling for ML experiments.

• Engage with business stakeholders, product managers, and operations teams to translate requirements into data deliverables.

• Communicate insights and findings in a clear, structured manner to technical and non-technical teams.

• Act as a central resource for data-driven decision-making across multiple verticals.

• Ensure data accuracy, consistency, and completeness across datasets.

• Maintain clear documentation of data sources, definitions, and processes.

• Work with engineering teams to fix broken pipelines, incorrect metrics, or data anomalies.

• 1-3 years of experience as a Data Analyst, Business Analyst, or Associate Data Scientist.

• Strong hands-on skills in:

• SQL (advanced level)

• Python (Pandas, NumPy, basic scikit-learn)

• Excel/Google Sheets

• Experience with BI tools such as Superset

• Solid understanding of:

• Descriptive statistics

• Basic probabilistic models

• Logistic regression, clustering concepts

• Ability to work with large datasets and perform efficient data wrangling.

• Exposure to ML pipelines, feature engineering, or model evaluation basics.

• Familiarity with A/B testing, experiments, hypothesis testing.

• Experience with data warehousing tools (BigQuery, Databricks, Snowflake).

• Basic familiarity with heuristic-based decision systems or rule engines.

• Knowledge of scripting automation for recurring reports.

• Understanding of business domains like content, fintech, OTT, growth, or operations.

Data Tools: SQL,Python, Pandas, NumPy
Visualization: Tableau, Power BI, Looker ML Basics: Scikit-learn, Statsmodels Data Warehousing: BigQuery, Databricks, Athena Workflow: Airflow Version Control: Git

• Bachelor s/Master s degree in Data Science, Statistics, Engineering, Computer Science, Economics, or related fields.

• Certifications in data analysis, SQL, or ML fundamentals are a plus.