Data Scientist
Experience Range: With at least 4 years of experience in advanced data science, statistical analysis, and machine learning model development, including hands-on work with large datasets and production model deployment. Key Responsibilities:
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
Required Skills:
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
Preferred Skills:
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
Desired Qualifications:
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)
