The ideal candidate combines deep expertise in statistical modeling, machine learning, and data engineering with a strong focus on Generative AI (GenAI), Large Language Models (LLMs), and Deep Learning.

This role requires both technical leadership and strategic visionfrom driving AI/ML innovation to mentoring junior team members, and collaborating across product, consulting, and engineering teams to bring AI-powered solutions to life.

By applying your expertise in data science to the ad-server and retail media domains, you will contribute to delivering targeted and impactful advertising campaigns, optimizing ad inventory, and maximizing ROI .

What will you do?

Your role in building ML algorithms for the ad server and retail media would involve the following :

Business Problem Solving :

- While technical proficiency in data manipulation, statistical modelling, and machine learning is crucial, the ability to apply these skills to solve real-world business problems is equally vital.

- Translate complex business challenges into analytical frameworks using statistical and ML methodologies.

- Apply AI/ML solutions to real-world business use cases across multiple industries.

- Extract insights from large-scale datasets to improve model accuracy, fairness, and relevance.

Technical Proficiencies :

- Strong expertise in statistical modelling, machine learning, deep learning, and NLP, with hands-on experience in LLMs and Generative AI (fine-tuning, RAG, prompt engineering).

- Proficiency in Python, PyTorch/TensorFlow, Hugging Face, and cloud-based ML platforms (AWS/GCP/Azure) with MLOps practices.

- Skilled in building scalable data pipelines and ML systems for training, evaluation, and deployment at scale.

- Solid grounding in Responsible AI principles including fairness, explainability, and bias mitigation.

AI/ML & Generative AI Innovation :

- Lead the design, development, and deployment of scalable ML and DL solutions, with emphasis on LLMs and GenAI.

- Build training, fine-tuning, evaluation, and serving pipelines for LLMs across use cases like content generation, summarization, semantic search, personalization, and multimodal AI.

- Drive applied research, incorporating the latest in GenAI and foundation models into production-ready systems.

- Ensure solutions are optimized for performance, security, reliability, and cost.

Leadership & Collaboration :

- Provide technical and strategic leadership to cross-functional teams of Data Scientists and Analysts.

- Collaborate with engineering and product teams to integrate AI solutions into customer-facing platforms.

- Mentor junior data scientists; set and promote best practices in MLOps, Responsible AI, and ML system design.

- Stay ahead of industry trends and incorporate them into roadmap planning.

Project Management & Strategy :

- Drive the execution of business plans and projects.

- Manage the continuous improvement of data science initiatives.

- Direct the gathering and assessment of data for project goals.

- Develop contingency plans and adapt to changing business needs.

You will be a great fit if you have :

- A master's or doctoral degree in a relevant field such as computer science, statistics, mathematics, or data science is preferred.

- A strong academic background with coursework in machine learning, statistical modeling, data mining, and programming is valuable.

- 8 -12 years of practical experience in data science, generative AI, machine learning, and analytics.

- Experience in relevant domains, such as e-commerce, advertising, or retail, may be advantageous.

- Minimum 34 years of deep, hands-on expertise in NLP, LLMs, and Generative AI.

- 2+ years of Demonstrated experience in leading projects and teams.

- Advanced Proficiency in Python is essential for data manipulation, statistical analysis, and machine learning model development.

Core Skills :

- Strong foundation in machine learning, deep learning, and NLP.

- Hands-on experience with LLMs (GPT, LLaMA, Falcon, Mistral, etc.) and GenAI frameworks (LangChain, Hugging Face, RAG, fine-tuning, LoRA).

- Proficiency in Python, PyTorch/TensorFlow, SQL, and cloud platforms (GCP/AWS/Azure).

- Experience with MLOps frameworks (MLflow, Kubeflow, Vertex AI, SageMaker, etc.

- Strong data engineering skills to build ETL pipelines for large-scale datasets.

- Statistical rigor : hypothesis testing, power analysis, confidence intervals; regression (GLM/GLMM), time-series; causal inference (propensity scores, DiD, RDD) for business impact.

Soft Skills :

- Strong business acumen and ability to map AI/ML solutions to strategic goals.

- Excellent communication and stakeholder management skills.

- Proven track record of mentoring and leading teams, collaborating with cross-functional teams, and communicating effectively with stakeholders.