JD Sr. Data Scientist
Experience: 6+ years
Location: Bangalore (Onsite)
Notice Period: Only candidates available to join within 15 days
Job Summary
Step into a Senior Data Scientist role specializing in MLOps and NLP to transform media analytics. Develop and launch state-of-the-art ML models, optimize production pipelines, and automate the full model lifecycle. You need 6+ months in media analytics or marketing effectiveness, alongside demonstrated MLOps, optimization, and NLP skills that deliver measurable business impact.
Required Skills & Experience
- 68 years in Data Science/Analytics, with expert-level MLOps and NLP capabilities.
- Bachelor's in Computer Science, Data Science, Statistics, Economics, Mathematics, or a similar quantitative discipline (Master's preferred).
- Advanced Python, Spark, and SQL skills for building robust, production-grade code.
- Hands-on MLOps expertise: CI/CD pipelines, automated testing, containerization (e.g., Docker), model monitoring, and deployment.
- Strong proficiency with Databricks, Snowflake, and Azure platforms.
- In-depth knowledge of LLMs, generative AI, and NLP applications.
- Experience with BI tools such as Power BI, Tableau, or DOMO.
- Expertise in ML algorithms, data structures, and designing scalable systems.
- Excellent analytical and problem-solving abilities in fast-paced environments.
- Strong communication and collaboration skills across global, cross-functional teams.
Desired Qualifications
- Background in Retail, eCommerce, or CPG industries.
- Skills in time series forecasting, customer lifetime value (CLV) modeling, and text classification.
- Knowledge of cloud-native MLOps tools (e.g., Azure ML, MLflow, Kubeflow).
Key Responsibilities
- Create, build, and deploy scalable ML/NLP models aligned with key business objectives.
- Develop and maintain comprehensive MLOps pipelines for training, validation, deployment, and monitoring.
- Work closely with stakeholders to translate intricate challenges into practical AI-driven insights.
- Lead media analytics initiatives to boost campaign ROI and effectiveness.
- Team up with data engineers, architects, and analysts to deliver seamless solutions.
- Automate data science workflows, experiment tracking, and governance.
- Champion advanced tools and best practices for ongoing optimization.
- Leverage LLMs and generative AI to tackle complex, real-world problems.
