Data Scientist - R01571772

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)