Translate business goals into analytical problems
Identify optimal algorithms, statistical techniques, traditional ML suitable for the business problem at hand
Work in cross-functional teams to develop ML/data science products
Apply best-in-breed data science techniques including descriptive, predictive, and machine learning methods from design to implementation
Focus on feature engineering, model training and model evaluation
Use AWS services including SageMaker, Lambda and other AI/ML services
Work with data warehousing, pipelines, and big data technologies such as AWS Glue for ETL, Glue Catalog, Glue Data Quality, and AWS Step Functions

Requirements

Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Engineering, or related field (or equivalent experience)
1-2 years of experience developing and implementing data science solutions
Proficient in Python for data science and application development
Experience writing complex SQL and PySpark queries to extract and integrate data from multiple sources
Solid understanding of hypothesis testing, causal inference, and model evaluation metrics
Experience with AWS, particularly SageMaker for training and deploying ML models
Proficiency in supervised and unsupervised models
Demonstrated experience in data transformation, manipulation, and working with structured vs. unstructured data
Strong understanding of APIs, microservices architecture, and cloud-native development

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLPySparkmachine learningdata transformationfeature engineeringmodel trainingmodel evaluationhypothesis testingcausal inference

Soft Skills

collaborationcommunicationproblem-solvinganalytical thinkingcross-functional teamwork