Senior Data Scientist - AdTech MarTech

Blend; Hyderabad; Sur place; INR 3 800 000 - 6 800 000; Plein temps

Avantages offerts par ce poste

Competitive Salary

Growth opportunities

Idea Tanks

Growth Chats

Snack Zone

Recognition & Rewards

Certifications covered

Résumé du poste

Blend in Hyderabad seeks a Senior Data Scientist to transform validated third‑party data into robust household‑level features powering audience intelligence. You will test data samples, assess match rates, and collaborate with Data Engineering to define schemas and data pipelines.

You will communicate findings clearly to non‑technical stakeholders and help identify privacy gaps for legal review, contributing to data quality and embedding‑driven projects.

Qualifications

• 6+ years of experience in data science or advanced analytics, ideally in adtech, martech, data brokerage or programmatic/real-time platforms.
• Statistical analysis of sample and panel data including match-rate testing, deduplication and bot detection, and geographic distribution analysis.
• Strong Python (pandas, scikit-learn or equivalent) and SQL.
• Working knowledge of identity resolution: hashed PII, deterministic vs. probabilistic matching, and MAID, HEM and household-ID concepts.
• Experience matching and joining disparate datasets, including third-party consumer and business data.
• Experience evaluating third-party or panel data quality: data dictionary review, provenance and overlap checks.
• Feature engineering for downstream ML and embedding use.
• Clear written communication of quantitative findings to non-technical stakeholders.

Responsabilités

• Evaluate third-party outcome, panel and video data against real data samples. Measure match rate, label precision, geographic distribution, deduplication and bot quality, and viewership precision.
• Design and run match-rate testing to independently validate provider-reported figures, using both deterministic and probabilistic matching approaches.
• Map provider-specific schemas and data dictionaries into a shared feature taxonomy.
• Build feature definitions and data-quality checks for a feature store supporting a household embedding model of roughly 100 features.
• Document data-quality findings in a standard scorecard format, with a clear evidence trail that others can reproduce.
• Identify privacy and consent gaps in third-party data, such as unclear consent for ML training, and elevate them for legal review.
• Partner with Data Engineering to define the validated schema each data pipeline should deliver.
• Present findings and recommendations to business and technical stakeholders.

Connaissances

Python (pandas, scikit-learn)

SQL

Identity resolution concepts

Data matching and joining datasets

Data quality assessment

Communication of quantitative findings

Experience in adtech/ Martech/ data‑dr

Outils

Python libraries

Snowflake

Databricks

MAID/HEM/household-ID concepts

Description du poste

Job Description:

Company Description

Blendis a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Job Description

We are looking for a Senior Data Scientist to join our outcome data pod. You will evaluate third-party consumer, business, panel and outcome data, and turn validated data into features for a household-level intelligence layer that powers audience and measurement use cases.

This is an analysis-led role. You will test what a data provider’s data actually delivers, using real samples, rather than relying on the provider’s reported numbers. You need a solid grasp of how the consumer and business data ecosystem works, including identity resolution, data licensing and audience data. You will work closely with Data Engineering and report findings that are sourced, reproducible and clear to non-technical stakeholders.

Key Responsibilities

• Evaluate third-party outcome, panel and video data against real data samples. Measure match rate, label precision, geographic distribution, deduplication and bot quality, and viewership precision.
• Design and run match-rate testing to independently validate provider-reported figures, using both deterministic and probabilistic matching approaches.
• Map provider-specific schemas and data dictionaries into a shared feature taxonomy.
• Build feature definitions and data-quality checks for a feature store supporting a household embedding model of roughly 100 features.
• Document data-quality findings in a standard scorecard format, with a clear evidence trail that others can reproduce.
• Identify privacy and consent gaps in third-party data, such as unclear consent for ML training, and elevate them for legal review.
• Partner with Data Engineering to define the validated schema each data pipeline should deliver.
• Present findings and recommendations to business and technical stakeholders.

Qualifications

Must Have Skills

• 6+ years of experience in data science or advanced analytics, ideally in adtech, martech, data brokerage or programmatic/real-time platforms.
• Statistical analysis of sample and panel data: match-rate testing, deduplication and bot detection, and geographic (DMA-level) distribution analysis.
• Strong Python (pandas, scikit-learn or equivalent) and SQL.
• Working knowledge of identity resolution: hashed PII, deterministic vs. probabilistic matching, and MAID, HEM and household-ID concepts.
• Experience matching and joining disparate datasets, including third-party consumer and business data.
• Experience evaluating third-party or panel data quality: data dictionary review, sample audits, provenance and overlap checks.
• Feature engineering for downstream ML and embedding use.
• Clear written communication of quantitative findings to non-technical stakeholders.

Good to Have

• Experience with feature stores or embedding-based customer or household representations.
• Exposure to marketing and media measurement, telecom or advertising audience data.
• Familiarity with data clean-room platforms (Snowflake Data Clean Rooms, LiveRamp, Habu, InfoSum).
• Experience with Snowflake or Databricks.
• Working familiarity with CCPA and consent frameworks, enough to recognize gaps rather than adjudicate them.

Additional Information

Thrive & Grow with Us

• Competitive Salary: Your skills and contributions are highly valued here, and we make sure your salary reflects that, rewarding you fairly for the knowledge and experience you bring to the table.
• Dynamic Career Growth: Our vibrant environment offers you the opportunity to grow rapidly, providing the right tools, mentorship, and experiences to fast-track your career.
• Idea Tanks: Innovation lives here. Our "Idea Tanks" are your playground to pitch, experiment, and collaborate on ideas that can shape the future.
• Growth Chats: Dive into our casual "Growth Chats" where you can learn from the best—whether it's over lunch or during a laid-back session with peers, it's the perfect space to grow your skills.
• Snack Zone: Stay fuelled and inspired! In our Snack Zone, you'll find a variety of snacks to keep your energy high and ideas flowing.
• Recognition & Rewards: We believe great work deserves to be recognized. Expect regular Hive-Fives, shoutouts and the chance to see your ideas come to life as part of our reward program.
• Fuel your Growth Journey with Certifications: We’re all about your growth groove! Level up your skills with our support as we cover the cost of your certifications.

Requirements