Data Scientist Python Spark

Data Scientist
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

Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, or a related field with 3-5 years’ experience
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) and 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
Job Description & Responsibilities
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
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
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)
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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