Data Scientist - R01562490

Data Scientist

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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: AI/ML Engineer

Job requirements

• Qualifications
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field with 3-5 years’ experience o Proven experience as a Data Engineer or similar role, with a focus on AI/ML projects.
• Strong proficiency in Python programming and experience with relevant libraries and frameworks (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
• Solid understanding of data engineering concepts, data modeling, and database systems (e.g., SQL, NoSQL).
• Experience with data integration and ETL tools (e.g., Apache Airflow, Apache Spark, Talend). o Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and related services (e.g., S3, EC2, BigQuery). 
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
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• Job Description & Responsibilities Data Pipeline Development:
• Design, develop, and maintain scalable and efficient data pipelines to collect, clean, and transform large volumes of data.
• Collaborate with software engineers, and other stakeholders to understand data requirements and implement effective solutions.
• Ensure data pipelines are robust, reliable, and optimized for performance. Data Modeling and Integration:
• Design and implement data models that support the storage, retrieval, and analysis of structured and unstructured data.
• Integrate and consolidate data from various sources, both internal and external, to create a unified and comprehensive data ecosystem.
• Ensure data integrity and accuracy through data quality assessments, cleansing, and validation techniques. Machine Learning Implementation:
• Optimize and enhance machine learning algorithms for performance, scalability, and accuracy.
• Implement data preprocessing, feature engineering, and model training workflows using Python and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch). Data Infrastructure Management
• Configure and maintain cloud-based infrastructure for data storage, processing, and analysis.
• Monitor and troubleshoot data-related issues, ensuring high availability and reliability of data systems.
• Stay up-to-date with emerging technologies, tools, and best practices in data engineering, AI, and ML. 

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