The Role
Leads the design, development, deployment, and monitoring of production-grade machine learning and statistical models for predictive, propensity, segmentation, and AI applications. The role manages the full data science lifecycle, builds scalable ML and MLOps pipelines using Python, Spark, and cloud platforms, explores GenAI and AI agents, defines performance metrics, communicates findings to stakeholders and clients, and mentors junior data scientists.
Summary Generated by Built In
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 Required
- 8+ years of hands-on experience in data science, machine learning, statistical modeling, or a related field
- Strong expertise in Python, Scikit-learn, Pandas, NumPy, and related data science libraries
- Strong proficiency in SQL and experience with large-scale datasets
- Strong hands-on experience with Spark or PySpark and distributed data processing
- Experience building, deploying, and optimizing production-grade machine learning models at scale
- Understanding of regression, classification, decision trees, random forests, gradient boosting, XGBoost, SVM, clustering, dimensionality reduction, neural networks, and deep learning
- Understanding of feature engineering, model training, hyperparameter tuning, model validation, performance evaluation, and optimization
- 5+ years of experience building ETL, ML, data transformation, or MLOps pipelines
- Hands-on experience with Databricks or other cloud-based ML or data platforms
- Experience with Spark MLlib, Scikit-learn, TensorFlow, Keras, or equivalent ML frameworks
- Understanding of MLOps and ML lifecycle management, including deployment, monitoring, versioning, automation, and model governance
- 1–2+ years of experience with GenAI or AI agents using platforms such as Claude, Databricks Genie, Snowflake Cortex, LangChain, or LangGraph
- Strong analytical and problem-solving capabilities
- Excellent communication and stakeholder management skills
- Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline
- Experience in propensity modeling, customer analytics, audience analytics, marketing analytics, AdTech, or MarTech
- Experience with demographic, behavioral, survey, digital marketing, media, or transactional datasets
- Experience with Databricks ML, MLflow, Unity Catalog, Feature Store, Mosaic AI, or similar platforms
- Experience with Airflow, Kubeflow, or other workflow and orchestration platforms
- Experience with H2O.ai or other AutoML platforms
- Strong experience with deep learning and neural network frameworks, including TensorFlow and Keras
- Exposure to LLMs, RAG, agentic AI, generative AI, and AI-powered automation
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The Company
What We Do
Anblicks is a Cloud Data Analytics Company based out of Dallas, TX, with offices in USA, India, and Australia. Since 2004, Anblicks has been helping customers by bringing value to their data and implementing modern data architecture and advanced analytics solutions in the cloud.
