Experience: 5–9 Years
Employment Type: Full-Time
We are looking for an experienced Senior Data Scientist with strong expertise in Machine Learning, Statistical Modelling, Python and Big Data technologies. The ideal candidate should have hands-on experience developing, deploying and optimizing end-to-end machine learning models using Python, PySpark, Spark MLlib and cloud platforms, preferably Microsoft Azure.
The role involves working with large-scale customer, transactional and web analytics datasets to develop predictive models and deliver actionable business insights. Candidates with experience in customer analytics, marketing analytics and distributed machine learning will be preferred.
Key Responsibilities
Analyze large volumes of customer, transactional, web analytics and third-party datasets to identify business trends and actionable insights.
Design, develop and implement machine learning models using Python, PySpark and Spark MLlib.
Develop predictive and statistical models using classification, regression, clustering and other ML algorithms.
Build and optimize end-to-end machine learning pipelines using Azure Databricks and Azure Data Factory.
Develop customer-focused ML models, including Churn Prediction, Customer Lifetime Value (CLV), Propensity and Affinity models.
Perform data extraction, cleansing, transformation, feature engineering and statistical analysis using Python and SQL.
Work with distributed computing frameworks and large-scale data processing environments.
Evaluate, validate, tune and optimize ML models to improve accuracy, scalability and performance.
Collaborate with business stakeholders, data engineers, developers and cross-functional teams to translate business problems into data science solutions.
Support model deployment, monitoring, version control and continuous integration using tools such as Git and Jenkins.
Mandatory Skills
Strong hands-on experience in Python programming and SQL.
Expertise in Machine Learning algorithms and predictive modelling.
Hands-on experience with PySpark and Apache Spark.
Experience with Spark MLlib and distributed machine learning.
Strong understanding of statistical modelling, probability and data mining techniques.
Experience working with large-scale datasets and distributed computing platforms.
Good to Have Skills
Experience with Azure Databricks, Azure Data Factory and Azure cloud services.
Knowledge of AWS cloud infrastructure.
Experience with TensorFlow, Keras and Deep Learning.
Familiarity with MLflow, Git, Jenkins and CI/CD pipelines.
Experience in Customer Analytics, Marketing Analytics, Churn Prediction, CLV and Propensity Modelling.
Educational Qualification
Master's degree in Computer Science, Mathematics, Physics or a related discipline, or equivalent education and professional experience.
Ideal Candidate Profile
5–9 years of experience in Data Science and Machine Learning.
Strong expertise in developing and deploying production-ready ML models.
Ability to independently handle end-to-end data science projects.
Strong analytical, problem-solving and communication skills.
Experience working with cross-functional and distributed teams.
Skills: azure,predictive modelling,machine learning,python,sql,statistical modelling
