Lead Data Scientist, Rail Data and Risk

Sperry Rail•Shelton, CT

About The Position

As Lead Data Scientist, Rail Data and Risk, you will build Sperry's view of where risk sits in our customers' track and how it is changing. We run non-stop inspection across North America and hold years of ultrasonic, induction, and eddy current test data. Your first job is descriptive: where defects and surface conditions are concentrated, and how they are trending. The second is predictive: where failure is likely, and what it would cost. These are different problems, and the expectation is that you compound toward all of them rather than arrive expert in each. What makes the role senior is the second half of it. The analysis only counts once it reaches the people who act on it – our commercial team, the analysts reviewing that track, and our customers. This is the first seat in a new US data science team. You will hire and line manage a data scientist working on analyst decision quality and a software engineer who puts models into production. Both join in the following quarter, so for the first two quarters this is majority hands-on work, with the team building around you rather than you waiting for it. The role carries real autonomy. The business case, the reporting line, and the recruiting channel are already in place; the methodology, the models, and the way this work reaches its audiences are yours to set. We hold a large amount of data and not everything we would ideally want, so part of the job is getting useful answers out of imperfect data and telling us what we should be collecting instead.

Requirements

Statistical depth: probability, hypothesis testing, regression analysis, time series analysis
SQL and Python, or equivalent analysis tooling
Risk modelling or reliability engineering
GIS or geospatial data analysis
Experience applying statistical and risk-modelling methods to physical or engineered systems – rail, industrial, energy, or a comparable setting
Demonstrable experience managing staff across the full employee lifecycle
A credible and confident communicator, written and verbal, at all levels of a business
Ability to make effective decisions and to keep calm under pressure
High level of honesty and integrity
A collaborative, team-first mindset aligned with our values of being Humble, Hungry, and Smart
Qualifications and years of experience are indicative guidelines, not mandatory requirements. These criteria may be met through demonstrated competency or equivalent experience.

Nice To Haves

Bachelor's degree in applied mathematics, statistics, physics, engineering, or a related quantitative field; advanced degree welcome
Typically 7+ years analysing large technical datasets
Linear referencing or corridor-level network data – for example pipeline integrity, highway, or utility corridor work
Rail or transportation specifically
Sensing technologies: ultrasonics, induction, eddy current

Responsibilities

Trend Sperry's multi-modal test data over time – internal flaw detection, induction, and eddy current – for defect growth and surface condition degradation across our non-stop inspection programs in North America
Connect raw test measurements and their metadata to the physical conditions they represent: internal defects, surface conditions, rail flaws
Analyze defect and error types and frequency by subdivision to identify where risk is concentrated and how it moves
Apply risk-based models to estimate the probability and consequence of failure
Own the KPIs for the monthly operational review and customer account review meetings
Present findings to the commercial team in a form they can use in account conversations
Work with the analysis organization so that what the data shows about defect and surface-condition patterns reaches the analysts reviewing that track
Present risk findings to customers alongside the commercial team
Identify gaps in current data collection and recommend what Sperry should capture to support better analysis
Validate findings against field conditions with track engineering and testing teams
Hire and line manage the two further seats in the pod, and direct their work
Write and maintain documentation so that the analysis is transferable rather than held tacitly