Senior Data Scientist

# Job Role : Senior Data Scientist
Location: Hyderabad, India Experience: 6+ Years
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## POSITION OVERVIEW
Anblicks is seeking an experienced Senior Data Scientist to lead the development of advanced statistical models, machine learning solutions, and analytical methodologies that transform complex datasets into actionable business insights. The ideal candidate will have strong expertise in applied statistics, causal inference, experimental design, marketing measurement, and predictive modeling, along with hands-on experience delivering production-ready data science solutions. In this role, you will take ownership of measurement methodologies, including incrementality and lift analysis, multi-touch attribution (MTA), marketing mix modeling (MMM), and optimization models. You will work with large-scale media, customer engagement, commerce, and business outcome datasets to develop scalable, repeatable analytical solutions. You will collaborate with Data Engineering, Product Management, and cross-functional teams to translate business requirements into robust modeling frameworks, mentor junior Data Scientists, and drive continuous improvement in model accuracy, scalability, and business impact.
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DUTIES AND RESPONSIBILITIES

Primary Responsibilities: Data Science, Measurement and Optimization

- Own the design, development, validation, and continuous improvement of statistical and machine learning models supporting business measurement and optimization use cases.
- Develop and maintain multi-touch attribution (MTA), marketing mix modeling (MMM), and rules-based attribution methodologies to measure the impact of marketing and business activities across multiple channels.
- Design and implement causal inference and incrementality measurement approaches to quantify the incremental impact of campaigns, customer engagement, and business initiatives.
- Develop and analyze experiments, including A/B tests, geo-based experiments, matched-market tests, conversion lift studies, and brand lift studies, to evaluate business outcomes.
- Perform exploratory data analysis, feature engineering, statistical analysis, and data preprocessing on large-scale datasets from multiple sources.
- Integrate and analyze media exposure, advertising performance, customer engagement, commerce, and conversion data to develop reliable measurement solutions.
- Apply media measurement concepts, including impressions, reach, frequency, deduplication, viewability, conversion windows, and cross-channel attribution.
- Develop optimization models to support budget allocation, channel-level performance optimization, response and saturation curve analysis, frequency recommendations, and campaign performance improvement.
- Define and standardize measurement metrics such as incremental return on ad spend (iROAS), cost per incremental acquisition, incremental reach, and lift estimates with confidence intervals.
- Translate business requirements and product strategies into modeling specifications, feature requirements, experimentation plans, and model validation criteria.
- Collaborate with Data Engineering teams to define feature pipelines, validate data quality, and ensure the availability and consistency of model-ready datasets.
- Evaluate and integrate new data sources, partner feeds, and external platform data into analytical and modeling workflows.
- Establish model evaluation frameworks, statistical validation procedures, performance benchmarks, and documentation standards.
- Monitor production models, investigate performance degradation, incorporate stakeholder feedback, and implement continuous improvements.
- Lead technical discussions, review model designs and code, establish data science best practices, and mentor junior Data Scientists.
- Communicate complex analytical methodologies, model assumptions, limitations, and business recommendations to technical teams and business stakeholders.

Secondary Responsibilities: Identity Analytics and Privacy-Aware Measurement

- Apply privacy-conscious analytical techniques to generate reliable insights from identity-linked, customer, and audience datasets.
- Work with identity resolution and graph-based data structures to analyze relationships between individuals, households, accounts, audiences, and business outcomes.
- Collaborate with Data Engineering, Identity, and Privacy teams to incorporate identity-linked datasets and governed data environments into analytical workflows.
- Develop analytical approaches that account for identity match rates, data coverage limitations, signal loss, and their impact on measurement confidence.
- Apply privacy-preserving analytical methods, such as aggregation thresholds, k-anonymity, and differential privacy, where appropriate.
- Develop scoring, segmentation, propensity, and predictive models using integrated first-party and third-party data.
- Support audience valuation, publisher measurement, inventory analysis, and closed-loop outcome measurement use cases.
- Contribute to analytical solutions involving secure data collaboration environments and data clean room platforms while following applicable privacy and governance requirements.

QUALIFICATIONS

Required Qualifications

- Bachelor's degree in Statistics, Data Science, Computer Science, Applied Mathematics, Economics, Engineering, or a related field. A Master's or PhD in a relevant discipline is preferred.
- 6+ years of experience in Data Science, Applied Statistics, Machine Learning, or Advanced Analytics, including ownership of production models or analytical deliverables.
- At least 3 years of hands-on experience in advertising or media measurement within an agency, publisher, AdTech, MarTech, or measurement-focused organization.
- Demonstrated experience developing causal inference and incrementality/lift models, along with hands-on expertise in at least one of marketing mix modeling (MMM) or multi-touch attribution (MTA).
- Strong proficiency in Python and/or R and SQL for data analysis, statistical modeling, and model development.
- Experience with statistical and machine learning libraries such as scikit-learn, statsmodels, PyMC, or equivalent frameworks.
- Strong understanding of statistical inference, regression analysis, experimental design, causal modeling, model evaluation, and validation techniques.
- Solid knowledge of media measurement concepts, including impressions, reach, frequency, deduplication, viewability, conversion measurement, and attribution windows.
- Hands-on experience working with advertising, media, retail media, or partner-platform data, including data from platforms such as Meta, Google, Amazon Marketing Cloud (AMC), or The Trade Desk.
- Experience with graph databases or graph-based data structures for identity resolution, entity relationships, or connected-data analytics, using technologies such as Neo4j, TigerGraph, or equivalent solutions.
- Strong analytical thinking, problem-solving, and statistical interpretation skills.
- Excellent communication and stakeholder-management skills, with the ability to explain complex modeling approaches and translate results into actionable business recommendations.

Preferred Qualifications

- Working knowledge of privacy-conscious measurement methodologies and data clean room platforms such as Snowflake Data Clean Rooms, LiveRamp, InfoSum, or equivalent solutions.
- Familiarity with cloud and data platforms such as Snowflake, Databricks, and AWS.
- Experience converting bespoke analytics or consulting engagements into standardized, scalable, and repeatable data products.
- Hands-on experience building optimization models for marketing spend allocation, channel performance, response curves, saturation analysis, and frequency optimization.
- Experience designing and evaluating media experiments, including geo-based tests, matched-market experiments, ghost-ad or public service announcement (PSA) controls, synthetic controls, conversion lift, and brand lift studies.
- Ability to reconcile experimental results with MMM and MTA outputs to develop consistent measurement methodologies.
- Exposure to Connected TV (CTV), linear TV measurement, cross-publisher analysis, halo effects, and path-to-purchase studies.
- Familiarity with APIs and integration patterns for advertising platforms, demand-side platforms (DSPs), supply-side platforms (SSPs), publishers, and retail media networks.
- Familiarity with industry standards and guidelines from the Interactive Advertising Bureau (IAB) and Media Rating Council (MRC).
- Understanding of privacy regulations such as GDPR, CCPA, and other applicable data protection requirements.
- Exposure to LLMs, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and AI-assisted development tools such as GitHub Copilot or Claude Code to accelerate analysis, modeling, and documentation.
- Understanding of best practices for validating AI-generated code, maintaining analytical accuracy, and applying AI-assisted or agentic workflows responsibly.