Data Scientist-Financial Services

Data Scientist focusing on Financial Services. The Data Team comprises a large, diverse team of engineers, analysts, data scientists, machine learning engineers, and product managers and the Platform, Data Science, Machine Learning and AI, and Analytics groups for the organization. As a Data Scientist for financial services you will apply your advanced analytical skills and machine learning experience to implement credit risk models, behavioral analytics and pricing models.

you will responsible for

- work with the Data Analysts and Data Scientists to analyse financial profiling data and find new high-impact opportunities.

- Experience with shuffling around data in cloud environments (preferably GCP: BigQuery, Pub/Sub, Dataflow) and performing the data munging required for finding new opportunities in our data.

- identify and build ML models to be utilized throughout the range of financial services use cases

- Credit scoring and gamification

- Collection optimization

- These models will be based on internal and external data, using behavioral analytics (random forests, clusterization, ensemble training, etc.) and you will support their production alization in collaboration with Machine Learning Engineers.

Required Hands-On Experience

- Experience in building financial risk models for a consumer-facing company

- Stakeholder management skills and the ability to manage timelines and expectations

- Strong hands-on experience in the ML life-cycle for data scientists (training, testing, tuning, and performance monitoring), and a good understanding of how your friendly Data Engineering and Data Ops colleagues deploy your models to production.

- Strong expertise in regularization techniques (Ridge, Lasso, elastic nets), Sampling techniques, variable selection techniques, feature creation (transformation, binning, high level categorical reduction) and validation (hold-outs, Cross-validation, bootstrap)

- Excellent Python for Data Science at scale skills (CI/CD, Python ML/DL)

Required Academic Qualifications

- Bachelor, Master degree from a top university in a quantitative field (Computer Science, Engineering, Mathematics, Statistics or similar), or equivalent experience

- Academic ability to conceptualize higher-order success metrics such as serendipity and exposure

- Very good theoretical understanding of relevant statistical models, their inner workings, assumptions, and limitations

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