Job Duties & Responsibilities
- Bring Creativity to Data products.
- Analyze transaction data.
- Build models to predict possible finance operation inconsistencies.
- Use machine learning methods to perform transaction matching (matching algorithms).
- Cleanse raw data for use in models.
- Perform data munging, data mining, clustering & classification methods, pattern recognition.
- Comfortable with Statistics, Calculus & Multivariate analysis.
- You will work closely with a small R&D team with Product Managers, Data Engineers & Analysts. The R&D team will experiment with the data and come up with POC's.
- Familiar with SQL, Python, R, Spark MLib, AWS, SQL Server, Redshift.
- Most of the data currently lives in SQL Server. We are experimenting with AWS Redshift as well. Python for coding and someone with experience with Spark MLib & Machine learning is a big plus.
- Extensive experience solving analytical problems using quantitative approaches.
- Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
- A strong passion for empirical research and for answering hard questions with data.
- A flexible analytic approach that allows for results at varying levels of precision.
- Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner.
- Familiarity with relational databases and SQL
- Expert knowledge of an analysis tool such as R, Matlab, or SAS
- Strong working knowledge of financials
- Acquired skills would include Data Warehousing, ETL, BI, Data Mining, and Machine Learning.
- Strong written and verbal skills – able to explain the work in plain language
- MS in Computer Science or other quantitative disciplines or BA with 2+ years applicable experience