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Open nowPosted 7 days ago

Data Scientist

Anblicks92 open roles

Where
Hyderabad
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Your applicationOpen nowData ScientistAnblicks · Hyderabad

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Share of postings closed within
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  2. 4.3%3 days
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  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 7 days ago

The posting

Job Role: Data Scientist

Location: Hyderabad, India

Experience: 3+ Years

POSITION OVERVIEW

Anblicks is seeking a motivated and analytical Data Scientist to develop, validate, and maintain statistical models and analytical solutions that transform complex datasets into actionable business insights. The ideal candidate will have a strong foundation in applied statistics, Python or R, SQL, machine learning, and exploratory data analysis, with an interest in solving business problems through data-driven methodologies.

In this role, you will work with large and diverse datasets to support predictive modeling, statistical analysis, causal inference, and performance measurement. You will collaborate with Data Engineers, senior Data Scientists, and cross-functional teams to develop reliable analytical workflows, validate data quality, and deliver repeatable, scalable data science solutions. Exposure to marketing analytics, attribution modeling, and privacy-conscious data analysis will be an added advantage.

DUTIES AND RESPONSIBILITIES

Primary Responsibilities: Data Science and Measurement Analytics

  • Support the design, development, testing, and validation of statistical and machine learning models for business measurement and analytical use cases.
  • Perform exploratory data analysis (EDA), data preprocessing, feature engineering, and statistical analysis on large, complex datasets.
  • Apply statistical modeling, hypothesis testing, experimental design, and causal inference techniques to identify patterns, measure outcomes, and generate actionable insights.
  • Develop and evaluate models supporting incrementality and lift analysis, multi-touch attribution (MTA), marketing mix modeling (MMM), and rules-based attribution, where applicable.
  • Analyze data from multiple sources, including customer interactions, media exposure, commerce, engagement, and business outcomes.
  • Collaborate with Data Engineering teams to validate data quality, feature pipelines, data transformations, and model input datasets.
  • Support the integration and validation of new data sources and partner feeds into analytical and modeling workflows.
  • Evaluate model performance using appropriate statistical metrics, validation techniques, and performance benchmarks.
  • Monitor deployed models and analytical workflows, identify data or performance issues, and recommend improvements.
  • Document analytical methodologies, modeling assumptions, experiments, results, and reusable best practices.
  • Translate business requirements into analytical problems and communicate findings, insights, and recommendations to technical and non-technical stakeholders.

Secondary Responsibilities: Identity Analytics and Privacy-Aware Data Science

  • Support privacy-conscious analytical approaches that generate meaningful aggregate insights while protecting sensitive and individual-level data.
  • Assist in developing scoring, segmentation, propensity, and predictive models using large-scale datasets.
  • Explore identity-linked datasets and graph-based analytical approaches to understand relationships, patterns, and audience behavior.
  • Contribute to analytical workflows involving secure data collaboration environments and data clean rooms, where applicable.
  • Follow data privacy, security, governance, and responsible data handling practices throughout the model development lifecycle.

QUALIFICATIONS

Required Qualifications

  • Bachelor's degree in Statistics, Computer Science, Data Science, Applied Mathematics, Economics, Engineering, or a related field.
  • 3+ years of experience in Data Science, Applied Statistics, Machine Learning, or Analytics.
  • Proficiency in Python or R and strong working knowledge of SQL for data extraction, transformation, and analysis.
  • Understanding of statistical modeling, probability, hypothesis testing, regression analysis, and experimental design.
  • Familiarity with causal inference concepts and the ability to interpret analytical results in a business context.
  • Experience with exploratory data analysis, feature engineering, model development, and model validation.
  • Ability to work with large, complex, multi-source datasets using cloud and data platforms such as Snowflake, Databricks, or AWS.
  • Understanding of machine learning fundamentals, model evaluation techniques, and analytical best practices.
  • Strong problem-solving, critical-thinking, and data interpretation skills.
  • Excellent communication skills, with the ability to explain analytical findings and recommendations clearly.

Preferred Qualifications

  • Exposure to marketing measurement methodologies, including incrementality testing, lift analysis, multi-touch attribution (MTA), marketing mix modeling (MMM), or rules-based attribution.
  • Familiarity with marketing and media metrics such as impressions, reach, frequency, conversion rates, and attribution windows.
  • Internship or professional experience in AdTech, MarTech, digital marketing analytics, customer analytics, or data-driven product environments.
  • Exposure to identity resolution, graph-based data structures, identity-linked analytics, or privacy-preserving data clean rooms.
  • Familiarity with data visualization and business intelligence tools such as Tableau, Looker, or Power BI.
  • Experience working with external data sources, APIs, partner integrations, publishers, or retail media platforms.
  • Coursework, certifications, or project experience in causal inference, A/B testing, experimentation, or statistical research.
  • Familiarity with machine learning libraries such as scikit-learn, pandas, NumPy, and relevant statistical modeling packages.
  • Exposure to LLMs, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and AI-assisted development tools such as GitHub Copilot or Claude Code.
  • Understanding of best practices for validating AI-generated code, ensuring analytical accuracy, and using AI-assisted workflows responsibly.
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