Data Analyst
Our core project is a leading financial services provider, specializing in payday loans. We focus on serving consumers seeking alternatives to traditional banking relationships by gaining convenient, immediate access to financial services.
Key responsibilities include:
  1. Deliver in-depth analysis - discover patterns and segments
  2. Collaborate with data scientists to improve accuracy of predictive models
  3. Create meaningful visual reports
Main requirements:
  • Experience in data mining
  • Strong knowledge of statistical concepts
  • Accuracy and attention to details
  • Experience with data visualization tools and packages
  • Experience with Python (Pandas, Numpy, Scipy, etc)
  • Experience with SQL (PostgreSQL)
As a plus:
  • Knowledge with machine learning concepts: regression and classification, clustering, anomaly detection, feature engineering\selection, ensembling\stacking etc.
  • Experience with cluster analysis
  • Experience in working with large datasets
  • Financial or banking background
Python developer for data science team
Our core project is a leading financial services provider, specializing in payday loans. We focus on serving consumers seeking alternatives to traditional banking relationships by gaining convenient, immediate access to financial services.
Key responsibilities include:
  1. Deploy machine learning models in production for their performance monitoring
  2. Collaborate with data scientists to build and maintain data pipelines
  3. Deliver high quality code and documentation
  4. Work with a mixed set of technologies and experiment with the new ones
Main requirements:
  • +2 years experience in development with Python
  • Experience with data structures, algorithms, design patterns
  • Experience with parallelization of complex operations, understanding of multiprocessing and threading
  • Experience with SQL (PostgreSQL) and NoSQL (Redis, MongoDB)
  • Knowledge of Docker
  • Passion about testing (Unit and Integration Testing)
  • Experience in working with different data types (CSV, Parquet, JSON)
  • Experience with version control systems (Git)
As a plus:
  • Experience with Apache Spark
  • Knowledge of database performance tuning
  • Experience in integrating NoSQL alongside RDBMS
  • Familiarity with Elasticsearch, Logstash, and Kibana or similar technologies
  • Experience with Python analytics libraries (​ Pandas, Numpy, Scikit-learn, Scipy, etc​ )
  • Familiarity with machine learning concepts: regression and classification, feature engineering\selection, ensembling\stacking etc.
Head of Risk
Key responsibilities include:
  1. Initiating and developing new approaches for customer analisis
  2. Introducing efficient lending procedures
  3. Coaching managers involved in Lending
  4. Credit portfolio and risk analysis, and recommending corrective actions
  5. Organizational capacity and managerial competence
Main requirements:
  • Knowledge of the methodology for assessing the borrower's risks in online
  • Development of scoring cards for various loan products
  • Development and maintenance of documentation of the process of creating / monitoring the work of the scoring card
  • Knowledge and skills in statistics, mathematical modeling
  • Development of automated reports for quality control of the scoring model
As a plus:
  • Experience with SQL (PostgreSQL)
  • Experience with Python (Pandas, Numpy, Scikit-learn, Scipy, etc)
Data Scientists
Data Scientists work with the risk-management and marketing teams to develop client relationships and deliver data science solutions for effective decisions. You will need to be comfortable leading technical demonstrations of SynergyOne, helping us develop models that add value to our projects, and integrating those models into project’s environments. Ideal candidates enjoy day-to-day data science problem-solving mixed with high levels of customer interaction.
Key responsibilities include:
  1. Build, test and validate predictive models
  2. Deliver high quality code and documentation
  3. Working with a mixed set of technologies and experimenting with new ones
  4. Exploring how state-of-the-art machine learning methods can be applied to our product
Main requirements:
  • Strong skills in exploratory data analysis
  • Experience with SQL (PostgreSQL)
  • Experience with Python (Pandas, Numpy, Scikit-learn, Scipy, etc)
  • Experience with machine learning concepts: regression and classification, clustering, feature engineering\selection, ensembling\stacking etc.
  • Familiarity with statistical concepts
As a plus:
  • Achievements on platforms for data science competitions (Kaggle, DrivenData, etc.)
  • Experience with data visualization tools and packages
  • Experience with version control systems (Git)
  • Experience with R
  • Experience with NoSQL (Redis)
  • Experience in deployment with Docker
  • Familiarity with CRISP-DM
  • Experience in building scoring models
  • Financial or banking background