Position: Middle Data Science Engineer + Welcome Bonus 3000 (only in Poland and Hungary) id 3844
Company: Exadel
Experience required: 2y
English level: intermediate

**About Our Customer:**
The сustomer is a leading provider of vehicle lifecycle solutions, enabling the companies that build, insure, repair, and replace vehicles to power the next generation of transportation. The company delivers advanced mobile, artificial intelligence, and connected car technologies through its platform, connecting a vibrant network of 350+ insurance companies, 24,000+ repair facilities, OEMs, hundreds of parts suppliers, and dozens of third-party data and service providers. The customer's collective set of solutions inform decision-making, enhance productivity, and help clients deliver faster and better experiences for end consumers. 

The сustomer’s company was ranked #17 in the Top 100 Digital Companies in Chicago in 2020 by Built in Chicago, an online community for digital technology entrepreneurs in Chicago, and was named one of Forbes best mid-sized companies to work for in 2019 – an important accolade and retention tool for the 2,600+ full-time company employees (alongside 350 dedicated contractors).

The сompany’s corporate headquarters is in downtown Chicago in the historic Merchandise Mart — a certified LEED (Leadership in Energy and Environmental Design) building that is also known to be a technology hub within the broader metro.


**About Our Project:**
Safekeep is a business unit that focuses on automating insurance claim subrogation for auto, workers compensation, property, and general liability claims.
SafeKeep leverages data analysis and AI/Machine learning packaged into a smart workflow engine to:

Identify claims with subrogation potential
Minimize the impact of subrogation team turnover
Decrease the administrative costs of subrogation
Simplify and optimize cross-carrier interaction

UI for the smart workflow engine is built specifically for subrogation business processes, bringing together data for decision-making about and tracking of the recovery process. This includes demand packages, settlements, and payments.
SafeKeep provides a digital platform for carrier-to-carrier collaboration to standardize and streamline the interaction.

**In 2020 alone, SafeKeep won the following awards:**
Innovation Championship by Zurich — out of 1300 solutions worldwide, SafeKeep won 1st place.
Innovation in Insurance awards — Out of 359 innovations from 45 different countries, Safekeep won the Global Silver Award, one of "the 3 best innovations at a global level" in InsurTech
Plug and Play Insurance Partners voted Safekeep as the #1 InsurTech

**Requirements:**
Experience performing machine learning/data scientist duties (e.g., ML algorithm selection, feature engineering, model training, hyperparameter tuning, distributed model training, supervised and unsupervised learning implementation, building model pipelines, using machine learning tools/libraries/frameworks)
Advanced knowledge of Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats)
Advanced knowledge of SQL to create complex pipelines that turn data into decisions
Proficiency in both written and verbal communication, sufficient for success in a remote and largely asynchronous work environment
Demonstrated capacity to clearly and concisely communicate complex technical, architectural, and/or organizational problems and propose thorough iterative solutions
Experience with performance and optimization problems and a demonstrated ability to both diagnose and prevent these problems
Experience owning a feature from concept to production, including proposal, discussion, and execution
Self-motivated and self-managed with strong organizational skills

**English level:**
Intermediate+

**Nice to Have:**
Experience with language modeling (e.g., transformers, Hugging Face, fastText, text cleaning)
Experience with AWS tools for modeling (Sagemaker, Comprehend, and Kendra)
A bachelor's degree in engineering, mathematics, or a related technical discipline
A master’s degree in computer science, mathematics, statistics, or other quantitative fields
Background in financial services including banking, insurance, or an equivalent field
Degree in computer science, engineering, mathematics, or a related field and 7+ years of industry experience
Experience creating orchestration workflows with tools such as Airflow, Kubeflow, or AWS Step Functions
DevOps experience (e.g., CI/CD Pipelines, Infrastructure as Code, containers, Agile software development)
Experience working with data streaming technologies (Kafka, Spark Streaming, etc.)
Proven success in communicating with customers (internal and external), other technical teams, and senior management in order to collect requirements, describe data modeling decisions and data engineering strategy
Knowledge of software engineering best practices across the development lifecycle, including Agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations