Data Specialist
Primary Skills
- Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Specialization
- Data Science Advanced: Data Specialist
Job requirements
- skills: strong experience with AWS Serverless Architecture (including API Gateway, AWS Lambda with a preference for Python, Amazon SQS, DynamoDB, and AWS Secrets Manager), proficiency in Python for backend and data processing, experience with event-driven and asynchronous architectures, a strong understanding of REST APIs and microservices design, and hands-on experience with Terraform for infrastructure as code. On the data and system design front, the ideal profile should also have experience with real-time data pipelines and streaming patterns, strong knowledge of NoSQL database design (particularly DynamoDB), an understanding of data validation, transformation and ingestion patterns, and the ability to design for high availability, scalability, and fault tolerance.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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