Data Scientist

Role Summary

We are looking for a Senior Data Scientist with strong hands-on experience in machine learning, deep learning, predictive modeling, and customer intelligence models such as affinity, propensity, and lookalike models. The ideal candidate should be comfortable working with large-scale datasets, building production-ready models, and collaborating with business, product, engineering, and marketing teams to drive measurable impact. The role also requires exposure to Agentic AI, including AI agents, LLM-based workflows, tool-using agents, and automation frameworks.

Key Responsibilities

● Build, validate, and deploy machine learning and deep learning models for business use cases.

● Develop affinity, propensity, recommendation, segmentation, churn, and lookalike models to improve targeting, personalization, and conversion.

● Work on end-to-end data science projects, including problem framing, data exploration, feature engineering, model development, evaluation, and deployment support.

● Analyze customer behavior, transaction patterns, content consumption, campaign responses, and digital engagement signals.

● Collaborate with product, marketing, business, and engineering teams to translate business problems into scalable data science solutions.

● Design experiments, A/B tests, uplift models, and measurement frameworks to assess model and campaign impact.

● Build scalable data pipelines and reusable modeling frameworks in partnership with data engineering teams.

● Apply deep learning techniques where relevant, including neural networks, embeddings, sequence models, NLP, or transformer-based models.

● Explore Agentic AI use cases such as AI assistants, workflow automation, autonomous task execution, and LLM-powered decision support.

● Present insights, model outcomes, and recommendations to technical and non-technical stakeholders.

● Mentor junior data scientists and help improve data science best practices.

Required Skills and Experience

● 5+ years of experience in data science, machine learning, or applied AI roles.

● Strong hands-on experience with ML algorithms such as regression, classification, clustering, random forests, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble methods.

● Experience building propensity models, affinity models, lookalike models, segmentation models, and recommendation systems.

● Good understanding of deep learning concepts and practical exposure to TensorFlow, PyTorch, Keras, or similar frameworks.

● Strong programming skills in Python and strong SQL skills for working with large datasets.

● Experience with data science libraries such as pandas, NumPy, scikit-learn, statsmodels, XGBoost, and LightGBM.

● Experience with model evaluation metrics such as AUC, precision, recall, F1-score, lift, gain, KS, RMSE, MAE, and business KPIs.

● Understanding of feature engineering, feature selection, model interpretability, and model monitoring.

● Exposure to cloud platforms such as AWS, GCP, or Azure is preferred.

● Exposure to Agentic AI, LLMs, prompt engineering, RAG, LangChain, LangGraph, CrewAI, AutoGen, OpenAI APIs, or similar frameworks.

● Ability to communicate complex analytical concepts clearly to business stakeholders.

Preferred Qualifications

● Experience in digital, media, e-commerce, subscription, advertising, fintech, or consumer-tech domains.

● Experience with personalization, audience intelligence, marketing analytics, campaign optimization, or customer lifecycle modeling.

● Exposure to MLOps tools such as MLflow, Airflow, Docker, Kubernetes, Kubeflow, Git, and CI/CD pipelines.

● Experience with vector databases, embeddings, RAG pipelines, or LLM-based applications.

● Understanding of data privacy, responsible AI, model governance, and ethical AI practices.

Educational Qualification

● Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or a related field.

● Advanced degree or relevant certifications in AI/ML will be an advantage.

Key Competencies

● Strong analytical and problem-solving mindset.

● Business-first approach to data science.

● Ability to work independently and manage multiple projects.

● Strong stakeholder management and communication skills.

● Curiosity to explore emerging AI technologies, especially Agentic AI and LLM-based systems.

● Ability to mentor and guide junior team members.