Experience Range: With at least 5 years of hands-on experience in advanced data science, including statistical analysis and machine learning, and up to 8 years in related roles Key Responsibilities:
Design and implement advanced statistical models, including hypothesis testing, regression analysis, and classification algorithms, to drive business outcomesConduct rigorous statistical analysis using t-tests, z-tests, and probabilistic graph models to extract actionable insights from large datasetsBuild, train, and deploy predictive models for forecasting and classification tasks using machine learning frameworks such as TensorFlow, PyTorch, and Sci-Kit LearnPerform data cleaning, transformation, and exploratory analysis utilizing Python, PySpark, R, and statistical tools like SAS or SPSSApply time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to predict trends and inform strategic decisionsDevelop and maintain data validation and monitoring pipelines to ensure data quality and model performanceCollaborate with cross-functional teams to translate business requirements into analytical solutions and communicate findings effectivelyAutomate machine learning workflows to streamline deployment and enhance scalabilityRequired Skills:
Expertise in hypothesis testing (t-test, z-test)Advanced regression analysis (linear and logistic)Programming proficiency in Python and PySparkHands-on experience with SAS or SPSS for statistical computingKnowledge of probabilistic graph modelsExperience with data validation frameworks such as Great ExpectationsTime series forecasting techniques (exponential smoothing, ARIMA, ARIMAX)Familiarity with classification algorithms (decision trees, SVM)Experience with machine learning frameworks (TensorFlow, PyTorch, Sci-Kit Learn)Proficiency in R for statistical modelingPreferred Skills:
Experience with distance metrics (Hamming, Euclidean, Manhattan)Expertise in model monitoring tools beyond Great Expectations, such as Evidently AIExperience in deploying models using BentoMLFamiliarity with orchestration tools for ML workflows, such as KubeFlowBackground in designing scalable machine learning pipelinesDesired Qualifications:
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related disciplineCertification in Data Science, Machine Learning, or Advanced Analytics (e.g., Microsoft Certified: Azure Data Scientist Associate, SAS Certified Data Scientist)Certification in Python or R programming (e.g., PCEP, PCAP, R Programming Certification)Additional Information: Immediate joiner required
