Polus is seeking a talented Data Science Analyst to join our dynamic team. In this role, your quantitative expertise will be instrumental in influencing decisions across the product lifecycle, from development to deployment.
Required Skills:
- Bachelor's or advanced degree in a quantitative field, such as Statistics, Mathematics, Computer Science, or a related discipline.
1-2 years of experience in a data science or related role.- Effective communication skills, with the ability to convey insights in non-technical terms to team members.
Solid understanding of statistical modeling and machine learning concepts.
- Proficient in programming languages such as Python, R, or SQL.
Eagerness to learn and stay updated on emerging data science technologies.- Basic project management skills, with a willingness to actively contribute to analytical projects.
Proficiency in Microsoft Excel and PowerPoint.
Preferred Skills:
- Assist in exploring and analyzing data, utilizing statistical and machine learning techniques to identify trends and patterns indicative of potential activities, with a specific focus on data related to new accounts and account takeovers.
Participate in implementing predictive models, specializing in topics such as new account detection and account takeover prevention, enhancing capabilities in these specific areas.- Work closely with senior team members, providing valuable analytical assistance in various tasks to contribute to product strategy development.
Stay updated on the latest data science techniques and tools, with a focus on AI and ML technologies relevant to detecting new accounts and account takeovers.
- Conduct retrospective analyses to demonstrate the value of data assets and solutions, providing insights into the tangible benefits for prospective customers.
Collaborate with the technical team to contribute to Quality Assurance testing processes, emphasizing the importance of ensuring the reliability and accuracy of machine algorithms and key data attributes.
Job Type: Full-time
Schedule: Monday to Friday
Experience:
- total work: 1 year (Preferred)
Work Location: In person
