Data Scientist Officer

About the Role

Data Scientist officer is responsible for using statistical and computational methods to analyze and interpret complex data sets.They work with large amounts of structured and unstructured data from various sources, including databases, social media, and sensors.

Work Responsibilities

• Develop and deploy machine learning/statistical solutions across a variety of business functions within the Digibank.
• Partner with stakeholders (product, engineering, operations, business, marketing, risk, etc.) across the bank to formulate solutions to complex and critical problems.
• Manage and own the entire end-to-end lifecycle of building and validating models along with their deployment and maintenance.
• Validate models on new datasets, based on in-market performance.
• Track model performance KPIs and improve performance of models developed.
• Engineer features from internal data assets to build refined customer profiles. Identify external data assets to bring into the model mix.
• Communicate complex technical concepts and results to non-technical stakeholders and cross-functional teams.
• Document models, methods, and data science best practices.
• Stay current on cutting edge machine learning tools and approaches.
• Be the data expert and ensure accuracy of data/results in their respective areas.
• Be an active participant in instilling positive data culture in the company.

Job Requirements

• 3+ years of experience in building and deploying machine learning models on large amounts of data.
• Degree in Statistics, Mathematics, Data Science, Computer Science, Analytics, Engineering, or other quantitative subjects.
• Extensive hands-on experience with programming languages such as Python, R, SQL.
• Experience with big data technologies such as Apache Spark and Hadoop.
• Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, etc.
• Deep technical and data science expertise, including experience in one or more analytical methods: statistical modeling, supervised learning, unsupervised learning, design of experiments, segmentation/clustering, text mining, network analysis, graphs, optimization, simulation, etc.
• Experience building in-production models, including associated scripting, error handling, and documentation.
• Understanding of trade-offs between model performance and business needs.
• Highly self-driven, critical thinker, and fast learner.
• Strong problem-solving skills and the ability to work independently and in a team environment.
• Has work experience and knowledge from more than one domain is a plus : 
• Risk Analytics, Marketing Analytics, Fraud analytics, Telecom analytics, Retail analytics, etc.

Leadership Competency

Individual Contributor