Data Scientist - Fintech

at  Skillspark AB

STARTASAP

DURATION7-9 months
emagine is looking for a Data Scientist to our client
Start: September/Oktober
Duration: end of March 2025
Location: Hybrid setup from Stockholm

JOB DESCRIPTION SUMMARY:

The Global Analytics and Data Science team is seeking a Data Scientists to join us as exceptional problem solver with a passion for data insights and a relentless execution focus.
As a Data Scientist, you will spearhead our efforts to measure and enhance our Payment Products. This role is central to our mission, offering you the opportunity to work with cutting-edge technology in a fast-paced environment. You will lead the data science projects for the initiatives that optimize transaction success rates, directly impacting our to offer seamless payment solutions to millions of users worldwide.

REQUIRED SKILLS AND QUALIFICATIONS:

• 2+ years of experience in analyzing multi-dimensional datasets and creating actionable business solutions.
• Strong educational background with a Bachelor’s/Master’s degree in Analytics, Engineering, Computer Science, Economics, Statistics, Mathematics, or a related quantitative discipline.
• Expertise in SQL, Excel, and visualization tools such as Looker, Tableau or Qlikview. Proficiency in a statistical programming language like R or Python is preferred.
• Thorough knowledge on A/B Experimentation setup and measurement.
• Demonstrable experience with big data technologies such as Teradata, Hadoop/Hive, and BigQuery.
• Knowledge of Python, Jupyter Notebooks, and SQL.
• Exceptional communication skills to effectively articulate insights and influence cross-functional teams.

• Data Analysis and Decision Making: Utilize large, complex datasets to develop structured data models that will help you to drive improvements in Product. Develop advanced analytical and statistical techniques to identify trends, issues, and opportunities. Some of the day to day tasks includes
• Data Collection and Preprocessing
• Exploratory Data Analysis
• Support in Experimentation
• Statistical Model Development and Implementation.
• Strategic Influence: Contribute to development of insights to formulate strategic recommendations to Product and Data Science leadership.
• Collaboration and Communication: Work closely with cross-functional teams, including product development, engineering, and marketing to implement data-driven improvements.