Senior Data Scientist
New York /
Rivet is building the strategic toolkit for capital productivity. 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. CFOs act as consiglieres to Boards and executive teams and wield a level of influence and autonomy in organizations that is only rivaled by the CEO. Despite this, most finance teams can only access software and services that were designed to solve problems from decades ago. Rivet is changing this.
Our mission is to ensure the most productive use of capital by unleashing finance and giving them unparalleled predictive capabilities and market access. We will shift finance from reactive decision-making to predictive and prescriptive reasoning — from the “what” to the “why”, the “so-what”, and the “what-if?” — and bring the leading edge of machine learning to finance.
The Rivet team is led by Nikhil Bharadwaj, formerly the Chief Operating Officer of Bloomberg L.P's Data organization, and is backed by leading early-stage investors. We are incredibly optimistic about our vision for finance, but equally aware that we have to figure out many details to bring it to life. We care deeply about the problem and get unreasonably excited about the turpentine of finance. We lead with the underdog mentality––we’re ambitious and relentless, but we don’t think we’re individually exceptional. This keeps us on our toes, so we’re continually learning from our customers and challenging and growing with each other.
We’re experienced builders and we’re 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 our customers. Come build with us!
About the role
As the founding data scientist at Rivet, you’ll have the opportunity to define and establish our foundational data science practices, drive the data science and machine learning roadmap, and be a leader on a team that will grow quickly as we scale. You’ll be the data science voice on Rivet’s senior team and contribute meaningfully to many other areas of the business and you will shape our culture, values, and ways of working in foundational ways.
What will you be doing in the role?
You will bring data and machine learning to the forefront of product development and decision making, and build data and machine learning pipelines for our financial intelligence engine, product KPIs, and forecasting applications. You will think boldly and experiment broadly to apply machine learning to solve customer problems. You’ll obsess about data-science design, prioritization, and execution and will work closely with the product, engineering, and design teams to increase customer value. You will be on the ground floor of Rivet’s Data Science team and mentor and lead junior members as the team scales. You’ll also work closely with the CEO and senior engineering and product leaders to set long-term strategy, build and recruit engineering and machine learning teams.
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 machine learning products that users love in new or rapidly evolving markets
- You flourish in uncertain environments and can turn incomplete, conflicting, or ambiguous inputs into solid data-science action plans
- You bring best practices to feature engineering, model development, and ML operations
- Your experience in deploying and monitoring the performance of models in production enables us to implement a best-in-class solution
- You have exceptional writing and speaking skills with a talent for articulating how data science can be applied to solve customer problems
- Graduate degree in engineering, data science, mathematics, physics, or another quantitative field
- 5+ years of hands-on experience in building and deploying production-grade ML models with ML frameworks (TensorFlow, Keras, PyTorch) and libraries like scikit-learn
- Track-record in building ML pipelines for time series, classification, and predictive applications
- Expert level skills in Python for data analysis and visualization, hypothesis testing, and model building
- Deep experience with ensemble ML approaches including random forests and xgboost, and experience with databases and querying models for structured and unstructured data
- A knack for using data visualization and analysis tools to tell a story
- You naturally think quantitatively about problems and work backward from a customer outcome
What’ll make you stand out (but not required)
- You have a keen awareness or interest in network analysis/graph analysis or NLP
- You have experience in distributed systems and graph databases
- You have a strong connection to finance teams or closely related domains, the challenges they face, and a deep appreciation for their aspirations