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Job: Senior Data Scientist/ML Engineer

TensorFlow, Python, SQL and relational databases
Remote
Full Time
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Client Overview

Our client is a company that equips CFOs and finance teams with the tools to be proactive with machine intelligence, real-time data, and predictive insights. Finance is a critical part of every enterprise. It sits at the center of almost all decisions and data flows and is charged with identifying the levers to improve business performance, and leading and validating capital allocation decisions. Our client is changing the way finance teams access software and services by bringing the leading edge of machine learning to finance.

Their vision is to unlock the true power of finance by giving teams the tools to be proactive. They aim to shift finance from reactive decision-making to predictive and prescriptive reasoning — from the “what” to the “why”, the “so-what”, and the “what-if?”.

They are an experienced team of builders and they are bringing together engineers, data scientists, product and design taste-makers, financiers, and others from a variety of backgrounds to experiment with new ideas and rethink old assumptions to create a new world for their customers.

About the role

As an early member of the engineering team at our client, you will have the opportunity to define and establish their foundational DS/ML engineering practices, drive the DS/ML roadmap, and be a leader on a team that will grow quickly as they scale. You’ll be the DS/ML voice on their senior team, contribute meaningfully to many other areas of the business, and you’ll shape their culture, values, and processes in foundational ways.

What will you be doing in the role?

You will bring data science and machine learning to the forefront of product development and decision making by performing exploratory data analysis, testing ideas through prototypes, building those ideas out into robust data and ML pipelines, deploying them to production, monitoring/analyzing the results, and pushing iterative improvements. On the technical side, you’ll obsess about architecture, documentation, and intuitive clean efficient code. On the business side, you’ll become an expert in the finance domain, think boldly, and experiment broadly to solve customer problems and increase customer value. You’ll be on the ground floor of our client’s DS/ML team, mentoring and leading junior members as they scale. You’ll also work closely with senior engineering and product leaders to set long-term strategy, and build and recruit a best-in-class team.

What would make you a good fit?

You’re both relentless and kind, and don’t see these as being mutually exclusive You have a self-directed learning style, an insatiable curiosity, and a hands-on execution mindset You have deep experience working with product and engineering teams to launch DS/ML products that users love in new or rapidly evolving markets You flourish in uncertain environments and can turn vague, incomplete, conflicting, or ambiguous inputs into solid action plans You bring standards and best practices to feature engineering, model development, and ML operations You have experience deploying, monitoring the performance of, and iterating on models in production You have exceptional writing and speaking skills with a talent for articulating how data science can be applied to solve customer problems

Must-Have Qualifications:

  • Degree in engineering, data science, mathematics, physics, or another quantitative field
  • 5+ years of experience in building and deploying production-grade statistical, ensemble ML, and deep learning models using frameworks and libraries (scikit-learn, TensorFlow, Keras, PyTorch)
  • 2+ years of experience building data/ML workflows with CI/CD
  • Proven track record in building products incorporating ML for time series, classification, and predictive applications
  • Expert level skills in Python, Pandas, and NumPy
  • Experience in handling large structured and unstructured datasets
  • Fluency with SQL and using relational databases to manage and analyze data
  • Knack for using data visualization and analysis tools to tell a story
  • You naturally think quantitatively about problems and work backwards from a customer outcome

What’ll make you stand out (but not required):

  • Familiarity with the tools for taking ML models to production on cloud platforms such as AWS
  • You have a strong connection to finance teams or closely related domains, the challenges they face, and a deep appreciation for their aspirations