- Location
- Hyderabad, India
- Department
- Education
- Seniority
- Senior
- Experience
- 5+ years
- Education
- Master
- Source
- Keka
Description
Job Role: Senior Data Scientist – AI/ML & Propensity Modeling
Location: Hyderabad
Experience: 8+ Years
About Anblicks
Anblicks is a Data & AI company helping global enterprises transform data into intelligent, scalable, and business-driven solutions. We specialize in Data Engineering, AI/ML, Cloud, Databricks, Snowflake, and Modern Data Platforms, delivering high-impact solutions for complex enterprise data and analytics challenges.
We are looking for a Senior Data Scientist to join our Data & AI team and lead the development of advanced machine learning, statistical modeling, predictive analytics, and AI solutions. This role is ideal for a hands-on Data Scientist who enjoys solving complex problems using large-scale data and building models that move from experimentation to production.
What You’ll Do
- Design, develop, evaluate, and deploy advanced machine learning and statistical models for predictive analytics, propensity modeling, segmentation, and data products.
- Apply statistical modeling, machine learning, optimization, and AI techniques to solve complex business and analytical problems.
- Work with large and diverse datasets, including demographic, behavioral, transactional, digital, media, and other structured/unstructured data.
- Drive the complete data science lifecycle — data preparation, feature engineering, model development, validation, optimization, deployment, and monitoring.
- Build and optimize models using supervised and unsupervised learning, ensemble methods, deep learning, and advanced statistical techniques.
- Develop production-grade ML solutions using Python, PySpark, Spark MLlib, Scikit-learn, TensorFlow/Keras, or equivalent frameworks.
- Design and implement scalable ML pipelines using Spark/PySpark and cloud-based data/ML platforms.
- Build and maintain ETL, ML, and MLOps pipelines supporting reliable production deployment.
- Implement automated approaches for model validation, monitoring, QA, performance tracking, and model/drift monitoring.
- Work closely with Product, Engineering, Data Engineering, and Business teams to translate business objectives into scalable data science solutions.
- Define relevant KPIs and model performance metrics and communicate insights to technical and business stakeholders.
- Drive adoption of modern Cloud, AI/ML, Databricks, and MLOps technologies across data science initiatives.
- Explore and implement GenAI and AI Agent solutions to automate analytical and business workflows.
- Partner with engineering teams to successfully transition models and data science solutions from development to production.
- Present analytical findings, model performance, and recommendations to senior stakeholders and clients.
- Mentor junior Data Scientists and contribute to technical standards, best practices, and innovation initiatives.
Required Skills & Experience
- 8+ years of hands-on experience in Data Science, Machine Learning, Statistical Modeling, or a related field.
- Strong expertise in Python, with hands-on experience in Scikit-learn, Pandas, NumPy, and related data science libraries.
- Strong proficiency in SQL and experience working with large-scale datasets.
- Strong hands-on experience with Spark/PySpark and distributed data processing.
- Proven experience building, deploying, and optimizing production-grade machine learning models at scale.
- Strong understanding of:
- Regression & Classification
- Decision Trees & Random Forest
- Gradient Boosting / XGBoost
- SVM
- Clustering
- Dimensionality Reduction
- Neural Networks / Deep Learning
- Strong understanding of feature engineering, model training, hyperparameter tuning, model validation, performance evaluation, and optimization.
- 5+ years of experience building ETL, ML, data transformation, and/or MLOps pipelines.
- Hands-on experience with Databricks or other cloud-based ML/data platforms.
- Experience with Spark MLlib, Scikit-learn, TensorFlow, Keras, or equivalent ML frameworks.
- Strong understanding of MLOps and ML lifecycle management, including deployment, monitoring, versioning, automation, and model governance.
- 1–2+ years of experience with GenAI / AI Agents, using technologies such as Claude, Databricks Genie, Snowflake Cortex, LangChain, LangGraph, or similar platforms.
- Strong analytical and problem-solving capabilities with the ability to translate ambiguous business problems into practical, scalable ML solutions.
- Excellent communication and stakeholder management skills, with the ability to present complex analytical concepts clearly.
- Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline.
Good to Have
- Experience in Propensity Modeling, Customer Analytics, Audience Analytics, Marketing Analytics, AdTech, or MarTech.
- Experience working with demographic, behavioral, survey, digital marketing, media, or transactional datasets.
- Hands-on experience with Databricks ML, MLflow, Unity Catalog, Feature Store, Mosaic AI, or similar platforms.
- Experience with Airflow, Kubeflow, or other workflow/orchestration platforms.
- Experience with H2O.ai or other AutoML platforms.
- Strong experience with Deep Learning / Neural Network frameworks, including TensorFlow and Keras.
- Exposure to LLMs, RAG, Agentic AI, Generative AI, and AI-powered automation.
What We’re Looking For
- A hands-on and technically strong Data Scientist who can take ML solutions from concept to production.
- Strong ownership and a problem-solving mindset with a focus on measurable business impact.
- Ability to work with complex, imperfect, and large-scale datasets while maintaining analytical rigor.
- Passion for emerging AI/ML technologies and a strong desire to experiment, learn, and innovate.
- Ability to collaborate effectively with global, cross-functional, and client-facing teams.
- Strong focus on building scalable, reliable, production-ready AI/ML solutions.
Skills
PythonSQLMachine LearningDeep LearningTensorFlowPandasNumPyScikit-learnSparkAirflowSnowflakeDatabricksData ScienceData EngineeringETL