Data Science Product Director

Location: New York, NY

Type: Full Time

Min. Experience: Experienced

Our client’s predictive AR workflow software leverages data and automatic payment communications to accelerate collections, enable teams to better manage customer relationships, and empower CFOs to better predict cashflow.  Our client’s platform is underpinned by big data and machine learning algorithms to better understand transactional and behavioral patterns to speed up the collection process.

Data Science Product Director

DESCRIPTION

Join our client’s team to make collecting money fast, easy, and highly predictive. In this role, reporting in through the Chief Product Officer, you will serve as analytics liaison between market and technical teams. You will drive results by delivering impactful analytic insights, improved data visualization, and advanced statistical analyses. Success in this role will come from transforming straight automation into highly predictive automation and highlighting key leading indicators to payment or user patterns. You will work with a high-performing delivery team to develop and deliver solution-oriented products that drive high levels of predictability for our customers.

The ideal candidate will have:

  • Business acumen and experience to generate customized, actionable insights in a data-driven and deadline-oriented environment.
  • Strong computer science and quantitative skills to extract maximum value from data assets.
  • The ability to look beyond numbers to understand how users interact with our products.
  • Hands-on business experience with statistical programming (SAS, R, Python) as well as advanced data science concepts (e.g., regression modeling, time series forecasting, classification, data clustering, time series forecasting, LSTM, CNN).
  • Experience with Natural Language Processing techniques and existing solutions on the market.
  • Customer-centric analytics & insights to support delivery teams in designing interventions to help our customers increase cash flow and predict their customer’s behaviors.

A day in the life of this position entails:

  • Participating in the design and prototyping of cutting edge algorithms to analyze payments and cash collection processes.
  • Collaborating with other highly skilled analytics colleagues within the delivery team to derive business insights from our data assets.
  • Rapidly developing a deep understanding of  data and processes across customers and their outstanding receivables.
  • Developing predictive models and machine learning algorithms to further automate the business’s back office.
  • Performing ad-hoc analyses by aggregating payment and customer data to unlock insights.
  • Structuring and executing against analytic road-maps to meet both short- and long-term needs of the market.
  • Diligently addressing obstacles as they appear.
  • Supporting the development and maintenance of data and analytics process documentation.
  • Building short-term and long-term data science strategies
  • Creating and maintaining business metrics for machine learning product development
  • Communicating data science ideas and approaches to company executives
  • Communicating business ideas to the data science team

REQUIREMENTS

  • Minimum of 10 years of experience delivering growth-oriented, quantitative solutions.
  • Minimum of 8 years of demonstrated Machine Learning and Data Science experience
  • BA/BS in Mathematics/Statistics, Economics (or other quantitative disciplines); Master’s degree highly preferred.
  • Relevant domain knowledge in accounts receivables, payments, finance, or treasury management
  • Expert knowledge of the math principles behind machine learning algorithms. You are unbeatable in statistics and classical regression and classification methods and can fluently implement them in Python using scikit-learn and Pandas.
  • Understanding of modern neural networks approaches, including CNNs and LSTMs. Familiarity and desire to work with TensorFlow is a plus.
  • Proficiency in processing large datasets (measured in hundreds of millions or billions of records) stored in traditional SQL and NoSQL data sources
  • Building and facilitating presentations to various audience: technical, non-technical and senior leaders
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