Description

You will develop and deploy analytical models to solve complex business problems using structured and unstructured data.

Responsibilities

• Translate business requests into specific analytical questions and communicate technical outcomes to non-technical stakeholders.
• Select and implement appropriate modeling techniques while considering data limitations and business needs.
• Develop, deploy, and document models within established guidelines and risk management frameworks.
• Collaborate with Data Engineering, IT, and governance teams to ensure solutions align with technical specifications and regulatory standards.
• Perform data cleansing, profiling, and manipulation to prepare datasets for advanced modeling.

Required Skills

• 10 to 12 years of relevant professional experience, including 6+ years in data science, machine learning, or quantitative analytics.
• Proficiency in Python, R, SAS, Power BI, and Alteryx.
• Strong expertise in SQL, HQL, and NoSQL for querying structured and unstructured databases.
• Experience with Time series Forecasting, Classification, Segmentation, Fraud Detection, NLP, and Deep Learning.
• Hands-on experience training and validating statistical and machine learning models.
• Ability to work with JSON, XML, and various delimited numeric data files.
• Skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
• Commitment to high code transparency through well-documented and commented code.
• Bachelor's or Master's degree in Computer Science, Information Systems, Statistics, or a related field.

Key Skills

Education

Bachelor's or Master's degrees