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Open nowPosted 525 days ago

Director, Data Scientist

Affinity Global Inc23 open roles

Where
Mumbai, India
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Your applicationOpen nowDirector, Data ScientistAffinity Global Inc · Mumbai, India

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  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 525 days ago

The posting

About Affinity

Affinity is pioneering new frontiers in AdTech: developing solutions that push past today’s limits and open up new opportunities. We are a global AdTech company helping publishers discover better ways to monetize and enabling advertisers to reach the right audiences through new touchpoints. Operating across 10+ markets in Asia, the US, and Europe with a team of over 450 experts, we are building privacy-first ad infrastructure that opens up opportunities beyond the walled gardens.

Role: Director, Data Science

Work Location: Mumbai (Malad)

About Role:

We are seeking a Head of Data Science to lead AI/ML initiatives across all our business units, driving measurable impact in digital advertising through sophisticated algorithms and team leadership. This high-impact role combines hands-on technical expertise with strategic vision, directly influencing millions in advertising revenue. You'll collaborate with C-level executives while building industry-leading AdTech solutions and establishing measurement frameworks that set new standards for performance. We're looking for a technical visionary who can balance algorithm development with strategic leadership across our global advertising ecosystem.

Roles & Responsibility:

  • Think Future, Build present – Create scalable solutions addressing current challenges while building frameworks for growth.
  • Design AI/ML algorithms for performance and programmatic advertising platforms with emphasis on floor price optimization and yield management.
  • Build bid prediction models and supply path optimization algorithms to maximize publisher revenue.
  • Develop algos and models which help various targeting for real-time ad delivery.
  • Implement audience segmentation and lookalike modelling for brand campaigns.
  • Think Data – Derive data insights from processes, products, and integrations to achieve efficiency and performance goals.
  • Establish KPI-driven measurement frameworks focused on incrementality gains and attribution accuracy.
  • Build predictive models for campaign forecasting and budget optimizations.
  • Develop fraud detection algorithms and brand safety classification systems.
  • Analyze data and identify trends, patterns, and anomalies in model behavior.
  • Ensure data privacy compliance (GDPR, CCPA) and implement secure data handling practices and participate in AI policy-making.
  • Think Technology – Build enterprise-grade ML/ AI architectural solutions that drive real value, and measurable business impact.
  • Develop MLOps and data pipelines from ad serving events, implementing real-time feature engineering and model serving infrastructure catering to billions of ads.
  • Build predictive models, dashboard / reports for performance monitoring.
  • Conduct rigorous A/B testing and statistical analysis to validate algorithmic improvements and business impact with explainable-AI algorithms.
  • Think Collaboration - Partner with cross-functional teams (stakeholders, product, developers and business) to deliver models, dashboards, solutions that drive revenue KPIs
  • Think Leadership - Drive strategic ML/AI vision across business units, build and scale high-performing teams, and own P&L responsibility for data science investments. Collaborate with fellow leaders to establish company-wide AI governance and present ROI metrics to executive leadership.

Required Skills:

  • 8+ years’ experience as Data Scientist with 3+ years in advertising technology and KPI optimisation
  • MS/PhD in Computer Science, Statistics, Mathematics, or related quantitative field.
  • Technical Expertise:Programming: Advanced Python, SQL, with experience in Hadoop and Apache Spark for large-scale data processing. ML/ AI stack: Tensorflow, PyTorch, XGBoost, LLMs, scikit-learn for time-series forecasting, recommendation systems, NLP optimisation, and causal inference. Exposure to ML, NN, GenAI algorithms. Infrastructure: Cloud platforms (GCP/Azure/AWS), MLOps, real-time model inference, feature stores, and ML pipeline orchestration Visualization: Power BI, Looker, Jupyter Notebooks, and custom dashboard developments Production Systems: Building scalable ML systems with real-time performance monitoring and A/B testing frameworks.
  • Domain KnowledgeDeep knowledge of digital marketing and advertising technologies and concepts like RTB protocols, header bidding, programmatic advertising ecosystems, and Google ADX. Understanding of Ad Server APIs, DSP/SSP integrations, DMP usage, auction dynamics, attribution modelling, conversion tracking, and audience segmentation. Proven track record of optimising AdTech KPIs with demonstrated results.
  • Leadership - Strong communication skills for technical and executive audiences with ability to translate KPI improvements into business impact.
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