{"id":3267031,"url":"https://alion.io/job/zee-entertainment-enterprises-director-data-science","title":"Director Data Science","company":{"id":1906943,"name":"Zee Entertainment Enterprises","domain":"zee.com","url":"https://alion.io/company/zee-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":54000,"max_usd":111000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":50},"experience_years_min":13,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Hadoop","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-10-26T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T09:35:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Responsibilities: \nLead, mentor, and grow a high-performing team of data scientists, ML engineers, and analysts.\nDefine the data science vision, roadmap, and strategy aligned with business objectives.\nPartner closely with Product, Engineering, Marketing, and Business leaders to identify high-value opportunities for AI/ML.\nPromote data-driven decision-making across the organisation.\nProvide hands-on guidance in designing, developing, and deploying advanced ML and AI models.\nOversee end-to-end lifecycle of data science projects, problem scoping, data exploration, modelling, evaluation, deployment, and monitoring.\nPersonalisation and Recommendation Systems (e. g., ranking models, user intent modelling, collaborative and content-based filtering).\nSearch Algorithms and Retrieval Optimisation (semantic search, vector search, query expansion).\nGenerative AI (LLMs, prompt engineering, RAG pipelines, content generation, automation).\nEnsure scalable, high-quality production ML systems in collaboration with engineering.\nPrioritise and manage multiple complex projects with cross-functional teams.\nTranslate business needs into technical requirements and actionable insights.\nCommunicate technical concepts to non-technical stakeholders effectively.\nEstablish best practices for experimentation, model governance, MLOps, monitoring, and continuous improvement.\nDevelop frameworks for model explainability, fairness, and compliance.\nOptimise processes to improve team efficiency and output quality.\n\nRequirements: \n11-15 years of experience in data science, machine learning, or AI roles, with at least 4-6 years in leadership or management.\nProven track record of building and deploying ML models at scale.\nPersonalisation and Recommendation Systems.\nSearch, Information Retrieval and Ranking.\nGenerative AI (LLMs, Transformers, RAG, fine-tuning).\nStrong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).\nExperience with distributed data processing (Spark, Hadoop) and cloud platforms (AWS/Azure/GCP).\nHands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, etc.\nExceptional problem-solving, communication, and leadership skills.\nAbility to thrive in fast-paced, ambiguous environments.\nMaster's in Computer Science, Data Science, Machine Learning, Statistics, or a related field.\nExperience working in product-led tech companies.\nExperience managing data science teams.\nFamiliarity with real-time ML systems and large-scale experimentation platforms.\nSkills\nLLM, LLMs, PyTorch, Python, RAG, Scikit-learn, 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