Job Description

Data Scientist

Job requirements

• Design, develop, and deploy advanced statistical and machine learning models using Python, R, and specialized frameworks to address complex business challenges
• Conduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling, to extract actionable insights from large-scale data
• Implement and validate data quality checks using tools such as Great Expectations and Evidently AI to ensure data and model integrity
• Collaborate with cross-functional teams to define data-driven strategies, translate business requirements into analytical solutions, and present findings to stakeholders
• Develop, optimize, and maintain forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to support business planning
• Build, train, and evaluate classification and regression models using ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras, MXNet, CNTK)
• Deploy and monitor models in production environments using scalable cloud-native tools such as KubeFlow and BentoML
• Document methodologies and contribute to continuous improvement of analytics best practices
• Advanced proficiency in Python and PySpark for data analysis and model development
• Expertise in statistical analysis and computing using SAS or SPSS
• Hands-on experience with regression techniques including linear and logistic regression
• Strong knowledge of hypothesis testing, including T-Test and Z-Test methodologies
• Proficient in building and interpreting probabilistic graphical models
• Experience with classification algorithms such as Decision Trees and Support Vector Machines (SVM)
• Skilled in forecasting techniques including exponential smoothing, ARIMA, and ARIMAX
• Familiarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance
• Working knowledge of R and R Studio for statistical modeling
• Experience with data validation and monitoring tools such as Great Expectations and Evidently AI
• Experience deploying machine learning models using KubeFlow or BentoML
• Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK
• Background in cloud-based analytics platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)
• Exposure to automated machine learning (AutoML) workflows
• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
• Certification in Data Science or Machine Learning from a recognized provider (e.g., Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate)