Data scientist

RESPONSIBILITIES

• Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develops and deploys models within the Model guidelines and framework.
• Composes technical documents for knowledge persistence, risk management, and technical review audiences. Consults with peers for guidance, as needed.
• Translates business request(s) into specific analytical questions, executing on the analysis and/or modeling, and communicating outcomes to non-technical business colleagues.
• Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions that are aligned with the customer's vision and specifications and consistent with modeling best practices and model risk management standards.
• Stay current with emerging trends and technologies in data quality management, data profiling, data cleansing tools, and AI/Client.
• Collaborate with data governance teams to ensure compliance with regulatory requirements and industry standards related to data quality and privacy.

QUALIFICATIONS

• 10 to 12 years of relevant experience, and 6+ years of experience in data science, machine learning, quantitative analytics (Mathematics, Statistics or Operational Research etc), quality assurance, and data management roles.
• Bachelor's or Master's degree in Computer Science, Statistics, or a related field (Mathematics, Operational Research, Data Science).
• Experience in training (Building) and validating statistical, machine learning, and other advanced analytics models.
• Experience in Time series Forecasting, Classification models, Segmentation, Fraud Detection, NLP, Deep Learning.
• Proficient in Python and Knowledge in R, SAS, and Tableau.
• Experience in using Client Libraries.
• Knowledge in Domino Data Lab, AWS Sagemaker is a plus.
• Excellent problem-solving, analytical skills and attention to detail, with the ability to identify patterns, trends, and anomalies in data.
• Ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
• Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
• Experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
• Strong communication and collaboration skills, with the ability to effectively interact with technical and non-technical stakeholders.