Data Scientist

Technology - Data Pleasanton, California


Description

Responsibilities:

  • Bring Creativity to Data products.
  • Apply machine learning methods to a variety of finance and accounting problems.
  • Responsible for building and maintaining the machine learning systems, data, platform and processes.
  • Build, integrate and deploy machine learning solutions into the BlackLine application in collaboration with product management, cloud, engineering and data science teams.
  • Perform qualitative and quantitative data analysis.
  • Cleanse and transform raw data used in machine models.
  • Perform data munging, data mining, clustering & classification methods, pattern recognition.
  • Comfortable with statistics, calculus and multivariate analysis.
  • Participate in ML POCs, validate the results and develop production implementations.
  • Build and optimize scalable machine learning solutions in the public cloud.
  • Familiar with SQL, Python, R, SparkML, TensorFlow, GCP, AWS, SQL Server.
  • Develop production systems in Python.
  • Work independently to research and solve business and technical problems.
  • Plan their work individually and as part of a team.
  • Mentor and train other Data Scientists on the team.

Qualifications:
  • Strong practical experience with machine learning techniques in the industry, accounting and financial industries is a plus.
  • Extensive experience solving analytical problems using quantitative approaches.
  • Experience with machine learning algorithms for building ML models, their accuracy, cleanliness, reliability.
  • Experience with predictive and prescriptive analyses, modeling, and segmentation.
  • Have strong passion for empirical data research for practical applications.
  • Ability to communicate complex quantitative analysis in clear, precise, and actionable
  • Comfortable with complex, high-volume, high-dimensionality data from varying sources.
  • Very comfortable with data engineering methods and pipelines.
  • Expert knowledge of analysis tools such as R, Matlab, or SAS
  • Experience with data warehousing, relational databases, ETL, BI, data mining.
  • Experience in SQL, R, Python languages.
  • Strong familiarity with GCP and AWS, SQL Server.
  • Practical experience with GIT version control.
  • Comfortable working with open source tools in a Unix/Linux environment.
  • Experience with and ownership of data-informed decision-making.
  • Experience translating business requirements into functional, and non-functional requirements.
  • Strong sense of product and data ownership.
  • Works independently without the need for supervision.
  • Strong written and verbal skills – able to explain the work in plain language.
  • MS/PhD in Computer Science or other quantitative disciplines