Data Scientist (Python, ML, SQL) in Chennai, India - Jobeax
Vacancy description
Data Scientist (Python, ML, SQL) in Chennai, India
talentgigs
India, Chennai
Data Scientist (Python, ML, SQL) in Chennai, India is listed on Jobeax. Browse 30,000+ vacancies available.
Data Scientist – Mortgage Analytics & AI Experience – 5 to 10 years About the Role We are looking for an experienced Data Scientist to join our team and help build the next generation of data-driven solutions for the mortgage and real estate industry. In this role, you will work at the intersection of data science, machine learning, cloud technology, and large-scale data engineering. You will have the opportunity to take ownership of complex analytical problems, develop production-grade machine learning solutions, and work with large volumes of mortgage, property, valuation, market, and transactional data. You will work closely with Data Engineering, Product, Technology, Analytics, and Business teams to turn complex business problems into scalable data science solutions. Mortgage domain experience is preferred but not required. We are equally interested in candidates with strong data science fundamentals who are excited to learn the mortgage and real estate domain.
Who You Are You are a hands-on Data Scientist who enjoys solving complex problems with data. You are comfortable working across the complete machine learning lifecycle — from understanding raw data and identifying patterns to feature engineering, model development, validation, deployment, and production monitoring. You are someone who can move between experimentation and production. You are equally comfortable writing Python to build a model, SQL to analyse hundreds of millions of records, and collaborating with engineers to operationalise a solution. You enjoy understanding the business problem behind the model and are able to communicate technical findings clearly to both technical and non-technical stakeholders. Most importantly, you are curious, analytical, and comfortable working in an environment where you are expected to explore, experiment, challenge assumptions, and continuously improve existing solutions. What You'll Do As a Data Scientist, you will:
Design, develop, and productionise machine learning and statistical models.
Work with large-scale structured and unstructured datasets.
Perform exploratory data analysis to identify patterns, trends, anomalies, and opportunities.
Develop advanced feature engineering and data transformation pipelines.
Build predictive models using techniques such as regression, tree-based models, gradient boosting, ensemble modelling, clustering, ranking, and deep learning where appropriate.
Develop end-to-end ML pipelines covering data preparation, training, validation, deployment, scoring, and monitoring.
Conduct model backtesting, benchmarking, hyperparameter optimisation, and performance analysis.
Develop model explainability and interpretability using techniques such as SHAP and feature importance.
Monitor model performance, data drift, feature drift, stability, coverage, and production accuracy.
Develop scalable batch and real-time prediction pipelines.
Build reusable analytical frameworks rather than one-off solutions.
Design automated data-quality and model-quality checks.
Investigate production issues and perform root-cause analysis.
Identify opportunities to improve existing models, data pipelines, and analytical methodologies.
Work closely with Data Engineers and Software Engineers to move models from experimentation into production.
Translate business problems into measurable data science objectives.
Present model results, insights, recommendations, and trade-offs to business stakeholders and leadership.
Technology You'll Work With Our environment includes modern cloud, data, and machine learning technologies. You may work with: Data & Cloud Platforms
Snowflake
Databricks
· AWS
Microsoft Azure
Google Cloud Platform
Programming & Data · Python
· SQL
PySpark
Pandas
NumPy
Machine Learning
Scikit-learn
XGBoost
LightGBM
CatBoost
MLflow
· SHAP
· TensorFlow / PyTorch Engineering & MLOps
Git
Docker
Kubernetes
REST APIs
FastAPI
· CI/CD
Model registries
Automated ML pipelines
Cloud-based model deployment and monitoring
You do not need experience with every technology listed above. We value strong fundamentals and the ability to learn new technologies quickly. What We're Looking For Required
Bachelor's or master's degree in data science, Computer Science, Statistics, Mathematics, Engineering, Economics, or another quantitative discipline.
Strong professional experience in Data Science, Machine Learning, Advanced Analytics, or a related field.
Strong proficiency in Python and SQL.
Strong understanding of machine learning and statistical modelling.
Experience developing predictive models using real-world datasets.
Experience with feature engineering, model evaluation, and model optimisation.
Experience working with large and complex datasets.
Strong understanding of model validation and performance metrics.
Ability to translate business requirements into analytical and machine learning solutions.
Strong problem-solving and analytical skills.
Ability to communicate complex technical concepts to different audiences.
What You'll Get This role provides the opportunity to:
Work on complex, real-world machine learning problems with measurable business impact.
Build models using large-scale mortgage and real estate datasets.
Work with modern cloud and data platforms such as Snowflake and Databricks.
Own solutions across the complete machine learning lifecycle.
Build production systems rather than proof-of-concept models alone.
Experiment with traditional machine learning, advanced analytics, and Generative AI.
Collaborate with experienced Data Scientists, Engineers, Product teams, and domain experts.
Influence the architecture and direction of data science solutions.
Continuously learn new technologies and modelling techniques.
See your models and analytical solutions directly influence products and business decisions.
If you are passionate about applying data science, machine learning, cloud technologies, and AI to real-world problems, we would love to hear from you.
... Experience with cloud-native data platforms and services such as: AWS: S3, Kinesis, DynamoDB, RDS, Glue, EMR, Redshift Azure: ADLS, ADF, Event Hubs, Synapse, Databricks, Azure SQL Strong programming skills in Python, Scala, or Java . Experience in Fraud Analytics, data lakes, ML pipelines, and modern data platform architectures.
... for an experienced Data Engineer to design, develop, and maintain scalable data platforms and pipelines using AWS, Apache Spark, Python, SQL, Kafka, and modern data engineering frameworks . The role will focus on building reliable data solutions for high-volume batch and real-time workloads, including data ingestion, transformation, ...
... opportunity for a Data Scientist to join a new and rapidly growing intraday team. In this role you will partner with our close-knit team of quantitative researchers, data engineers, technologists and data sourcing colleagues to research, engineer, and validate quantitative signals derived from high-frequency equity market data ...
... obvious, using intelligence, passion and creativity to inspire new thinking and shape the world we live in. Job Title: Data Scientist We're the world's leading data, insights, and consulting company we shape the brands of tomorrow by better understanding people everywhere. We are looking for a Data Scientist who will work ...
... for retraining Requirements - Experience: 6+ years data engineering focused on ML data pipelines - Python: Strong — pandas, pyarrow, JSONL processing at scale - ML Data Libraries: HuggingFace Datasets, Arrow-based storage - Data Formats: Multi-turn conversation/chat data structures and tokenizer-specific formatting - Deduplication: ...
... Requirements : - 3+ years of professional AI/ML engineering experience with a track record of delivering production-grade AI systems.- Strong programming skills in Python and SQL, with hands-on experience in ML libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow).- Hands-on experience building ...
... Requirements : - 3+ years of professional AI/ML engineering experience with a track record of delivering production-grade AI systems.- Strong programming skills in Python and SQL, with hands-on experience in ML libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow).- Hands-on experience building ...
... workflows. Collaborate with ML engineers and researchers to design challenging, real-world evaluation scenarios for MLE Bench. Minimum 3+ years of experience as a Data Analyst or Analytics-focused Engineer . Strong proficiency in Python for data analysis. Solid experience with SQL and relational datasets. Experience analyzing ...
... mapping, and change detection. - Ensure annotation accuracy, consistency, and adherence to defined quality standards (QA/QC frameworks) across all delivered datasets. - Define and continuously improve annotation guidelines, taxonomies, and labeling protocols in collaboration with Data Science and ML teams. - Stay current ...
... improve platform performance and resilience. Integrate with customer data platforms and pipelines, including bespoke data frameworks. 4–8 years of experience in data engineering or backend development in data-intensive environments. Proficient in Python and SQL; Strong experience with cloud-native data tools and services (S3, ...
... technical authority on internal data contracts and API schemas in cross-functional discussions with backend, product, and AI/ML teams 5+ years of experience in data engineering, with a proven track record delivering production-grade pipelines, data models, and storage architectures - Deep SQL expertise; hands-on experience ...
... delimited), APIs, XML, JSON). - Experience implementing data integration techniques such as event/message based integration (Kafka, Google Pub/Sub). - Advanced SQL skills; solid understanding of relational databases and business data; ability to write complex SQL queries against a variety of data sources. - Strong understanding ...
Data Engineer: PySpark+AWS Glue 4-8 years in Data Engineering with hands-on expertise in Snowflake, AWS Glue, AWS (S3/Lambda/CloudWatch), Python, PySpark, and SQL. Strong experience in ETL/ELT, Data Warehousing, Data Pipelines, Performance Tuning, and Cloud Data Platforms. Exposure to AI/ML or GenAI solutions is a plus ...
... demonstrated track record of delivering production-grade AI systems in real-world environments.- 3. Mandatory (Tech skill 1) : Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design.- 4. Mandatory ...
... and implement effective solutions.- Participate in design discussions, code reviews, technical documentation, and architecture decisions.- Work closely with Data Scientists, AI/ML Engineers, Architects, QA, DevOps, and Product teams.- Follow Agile/Scrum practices and contribute across the complete software development ...
... in NLP (clustering, summarization, sentiment analysis). Hands-on with AWS AI/ML stack. Familiarity with MongoDB & NoSQL/SQL. Experience with speech/multimodal AI. Exposure to data visualization tools. Build an AI-first SaaS platform solving real-world problems. Work with cutting-edge AI/ML and cloud-native architecture.
... in driving value for our customers by building data solutions. You'll be carrying out data engineering tasks to build, maintain, test and optimise a scalable data architecture, as well as carrying out data extractions, transforming data to make it usable to data analysts and scientists, and loading data into data platforms. ...
... understanding of event-driven architecture and real-time data streaming patterns Familiarity with Avro/JSON schema management and Schema Registry Experience integrating data between legacy and modern platforms Strong SQL/Python skills and experience working with relational databases Understanding of data mapping, data quality, and ...