:Primary Responsibilities & Objectives
Collect/manage diverse data to generate curated data sets for ease of interpretation and reporting
Perform complex data analysis of risk strategies performance, accuracy and on-going monitoring
A strong understanding of data engineering/science languages and practices (e.g. keras, tensorflow, scikit-learn, numpy, pandas etc
Ability to set up and maintain production pipelines for machine learning/deep learning applications from backend to frontend
Develop self-service reports and dashboards "Own" data sets consumed by internal customers, both from quality and performance (SLA) prospective
Build strong relationships and collaborate with teammates and business leaders
Automate workforce metrics and dashboards to deliver insights at scale
Analyze large data sets to distill insights from data and connect disparate ideas into cohesive, well-grounded recommendations & executive presentations
Determine relevant approach for predictive analytics, including data requirements, trade-offs in analytical methods, and interpretation of results
Ensure data quality processes; drive consistent use and adoption of measures, hierarchies, tools and reporting standards
Develop methodologies for analyzing and presenting data in an effective manner
Communicate and present analysis to a broad audience, including senior management
Deeply understand our users and their actions
:Qualifications, Skills and Education
B.A./B.S. degree from a leading academic institution; quantitative or technical degree a plus
Working experience in investigations, data analysis, and/or computer science in the technology space
Experience writing high quality code in Python plus another OOP language (Java, Scala, C++, Go, etc
Experience working with RDBM systems, particularly familiarity with SQL and MongoDB
3+ years experience building distributed solutions in Spark, MapReduce or other MPP system with associated data models and datastores (e.g., Redshift, Cassandra, HBase, Parquet
Production development of event-based applications using frameworks such as Kinesis, Kafka, Spark Streaming, or similar
Familiarity with machine learning techniques, continuous deployment pipelines and tools, and AWS technology stack a plus
Desire to work across internal teams to identify requirements and iterate on solutions
Debug complex production issues across various levels of the tech stack
Experience/familiarity with Slack, Mac OSX and GSuite
:Benefits
FinTech Start-Up, financed by industry relevant investors and partners
Autonomous hiring of a long-term team that allows for early-on shaping of the company’s culture
Diverse activities and autonomous implementation of own ideas in a dynamic environment
Sparring and support from top-class industry and technology experts from the investor and partner network
First-class technical equipment and software of your choice
A collaborative and creative work environment, with the possibility to adopt responsibility quickly
See how your knowledge and skills shape products
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