Position: Senior Data Scientist
Company: Intetics
Category: SQL
Experience required: 3y
English level: intermediate

About the project:

The project is a BI Solution based on the AWS cloud platform and processing 2 terabytes of data per week.

Project Stack: Data Lake, DWH, Data Analytics in AWS.



Responsibilities:

● Build ML models for a variety of applications, including credit risk modelling, user behavior modeling, communication channel recommendation, etc.;

● Work hand-in-hand with our Data Engineering team to deploy models at scale;

● Collaborate with teams across the organization such as product, engineering, operations, and marketing to drive data science initiatives from ideation to productionization;

● Work closely with Product, Engineering, and Data leadership to make sound technical decisions across the entire data ecosystem;

● Become the Subject Matter Expert for all data science-related topics;

● Keep track of industry trends, best practices, and technologies to continually improve our technology (and ourselves!);

● Contribute to the hiring, mentorship, and management of junior data scientists (as the Data team expands).

Job requirements:

● 3-5 years of experience in data scientist, actuary, or statistician role(s);

● Extensive experience with OOP (Python);

● Strong knowledge of major Python packages for data processing (e.g. numpy, pandas), ML (e.g. statsmodels, sklearn, xgboost, keras), and visualization (e.g. seaborn);

● Ability to develop ML models from scratch and to bring them to production;

● Strong knowledge of best practices for data science;

● Good English communication skills and ability to perform in fast-paced and multicultural environment.

Nice to have:

● Working knowledge of cloud data architectures (e.g. AWS);

● Working knowledge of SQL and RDBMS concepts (e.g. PostgreSQL, MySQL);

● Experience with credit risk modelling and/or in fintech;

● Experience with A/B & multivariate testing;

● Experience with visualization tools (e.g. Metabase);

● Experience with NLP;

● Experience working in agile environments (we recently moved from Kanban to Scrum).