Senior Data Scientist

Lead full life-cycle Data Science solutions from model development to deployment and monitoring.,Partner with engineering team for best practices in ML model deployment.,Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics.,Recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.,Utilize Python, Apache Spark, PySpark, R, Scala, SQL, NoSQL to obtain, integrate, manipulate, and analyze data.,Expertise in statistical data analysis (e.g., univariate/bivariate analysis) and data quality assessment.,Build Machine Learning, Deep Learning, and statistical models to solve business problems.,Develop predictive data models, anomaly detection models, quantitative analyses, and visualizations of big data sources.,Lead data exploration and analytic projects and provide coaching on big data topics.,Explore and implement semantic data capabilities through NLP, text mining, and machine learning.,Oversee data acquisition and ingestion from structured and unstructured sources, ensuring quality.,Utilize APIs to collect data from various products into the Data Warehouse Database.

5+ years of industry experience working on real-world problems.,University, Master, or Ph.D. degree in an analytical field (e.g., Computer Science, Engineering, Mathematics, Statistics, or related quantitative field).,Experienced with AI/ML infrastructure and model deployment for Gen AI applications in production environments and supporting enterprise-scale use cases.,Strong foundation in ML and AI basics, knowledge of Inferencing, fine-tuning, model architectures, Embeddings.,Hands-on experience implementing solutions using modern ML and Deep Learning frameworks (PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers).,Hands-on experience designing graph data models and working with graph databases (Neo4j, Amazon Neptune, TigerGraph) and/or knowledge graph frameworks (RDF/OWL, property graphs, SPARKQL).,Familiar with software engineering industry best practices (coding standards, testing methods, code reviews, version control).,Experience working with technical and non-technical project stakeholders to scope, formulate, deploy, and maintain data science systems.,Self-driven problem solver, able to adapt and thrive in a dynamic, ambiguous, and customer-faced environment.,Familiarity with GIT (GitHub).,Ability to prioritize work and manage multiple work streams concurrently.,In-depth knowledge of machine learning and deep learning algorithms.,Excellent working with structured and non-structured data.,Excellent knowledge in Python, PySpark, SQL.,Experience with cloud-based data platforms such as Azure or AWS.,Experience with data visualization tools such as Tableau, Looker, and Power BI.

University, Master or Ph.D. degree in an analytical field (e.g. Computer Science, Engineering, Mathematics, Statistics, or related quantitative field)