Senior Applied Scientist

DAT Freight & Analytics

Senior Applied Scientist Data Science - P3

Bangalore, India

About DAT

DAT is an award-winning employer of choice and a next-generation SaaS technology company that has been at the leading edge of innovation in transportation supply chain logistics for 45 years. We continue to transform the industry year over year, deploying a suite of software solutions to millions of customers every day — customers who depend on DAT for the most relevant data and most accurate insights to help them make smarter business decisions and run their companies more profitably.

We operate the largest freight marketplace of its kind in North America, with 400 million freights posted in 2022 and a database of $150 billion of annual global shipment market transaction data. We have office locations in Denver, CO, Beaverton, OR, Seattle, WA, Springfield, MO, and Bangalore, India.

The Opportunity

DAT’s Data Science team is seeking a Senior Applied Scientist: a hands-on Owner who takes full end-to-end responsibility for one or more models, services, or decisioning components that directly shape how freight moves across North America. At this level, you’re not waiting for a problem to be handed to you: you define the roadmap for your workstream, coordinate across Product, Engineering, and Operations, and ship solutions that have clear, measurable business outcomes. 

You will work largely independently, choosing the right blend of machine learning, statistical methods, and engineering rigor to solve moderately-to-highly ambiguous problems. You will lead medium-to-large projects, mentor junior scientists, and begin contributing to the scientific direction of the broader team.

Key Job Responsibilities

Service Ownership and Technical Delivery

• Own one or more ML models, APIs, or data pipelines end-to-end — including roadmap input, quality, monitoring, and coordination with Engineering and Product. Note: DAT is actively building out its MLOps platform, and the expected shape of end-to-end ownership for this role will be informed by that engagement. Candidates should be prepared to discuss both full-stack ML ownership and collaborative productionization models.
• Lead medium-to-large, cross-functional projects; break work into clear milestones and delegate effectively.
• Deliver production-quality Python code: well-documented, well-tested, efficient, and integrated into operational systems.
• Apply deep knowledge of one or more areas — machine learning algorithms, statistical modeling, experimental design, recommendation systems, optimization, or predictive statistical methods — to drive real improvements in how DAT understands customer behavior.
• Transform research findings into scored, operationalized outputs that flow into CS tooling, Sales prioritization systems, and product surfaces. Iterate from prototype to production with a strong eye for maintainability and reliability.

Analysis, Experimentation, and Scientific Rigor

• Develop solutions for moderately ambiguous problems, choosing appropriate depth and methods while working largely independently.
• Design and execute experiments with clear hypotheses, guardrails, and success metrics. Communicate findings to cross-functional stakeholders.
• Produce technical artifacts (Jupyter notebooks, validation docs, model write-ups) that are accessible to non-technical audiences and recommend clear actions.
• Apply strong analytical reasoning: question initial results, explore alternative explanations, and be explicit about assumptions and limitations.

Cross-Functional Collaboration and Leadership

• Build alignment and reduce ambiguity across PM, Engineering, and Operations partners. Break complex problems into incremental steps and define clear MVP scopes.
• Articulate a coherent vision for your workstream: how models and systems change decisions and customer outcomes over time.
• Actively coach and mentor squad members who are developing toward applied science careers. Be a reliable go-to resource for technical and career questions.
• Participate in hiring, calibration, and onboarding, helping define what good looks like in scientific work.
• Help establish and improve best practices for your workstream: framing, methods, evaluation, and which analytical approaches fit which decisions.

What You'll Do

As a Senior Applied Scientist on DAT’s Advanced Analytics Squad, you will build and maintain predictive models that help DAT understand and retain its customers at scale. Your core focus will be customer lifecycle analytics — churn risk modeling, behavioral segmentation, customer health scoring, and propensity modeling. Your work produces both operationalized scored outputs consumed by Customer Success and Sales, and analytical insights that inform product, pricing, and go-to-market decisions.

You will apply recommendation and scoring techniques to customer product adoption and expansion signals, helping surface the right product experiences for the right customer segments. You will also design and analyze rigorous experiments to accurately measure the impact of new features and pricing strategies, applying causal inference methods to ensure we’re learning what we think we’re learning.

Beyond your individual contributions, you will set technical direction by mentoring squad members and delivering solutions whose impact scales across the broader DAT organization. You will lead cross-functional collaborations with product managers, engineers, and data scientists to ensure modeling solutions integrate seamlessly into operational systems. The squad’s work also touches on longer-arc problems in network analysis, quasi-experimental methods, and anomaly detection as DAT’s product surfaces continue to grow.

Throughout all of this, you will stay current with the latest advances in machine learning and behavioral modeling, bringing that knowledge back to the team to keep DAT at the competitive edge of the industry.

Basic Qualifications

• MS or PhD in Computer Science, Statistics, Applied Mathematics / Operations Research, Engineering, or a related quantitative field. Candidates with 7 or more years of directly relevant industry experience may be considered in lieu of an advanced degree.
• 4+ years of experience developing and deploying machine learning or analytical solutions in production environments.
• Proficiency in Python; experience contributing to and maintaining ML codebases.
• Strong working knowledge of one or more of: supervised/unsupervised ML, time-series forecasting, experimental design, optimization, or recommendation systems.
• Proven ability to own a service or component end-to-end: roadmap, quality, monitoring, and cross-functional coordination.
• Experience designing and communicating experiments and model validations to both technical and non-technical audiences.
• Strong SQL skills for data exploration, feature creation, and diagnostic analysis.
• Track record of taking moderately ambiguous projects from problem statement to recommendation with limited guidance.

Preferred Qualifications

• Experience applying ML to customer lifecycle problems such as retention modeling, behavioral segmentation, health scoring, or propensity modeling, ideally in a B2B SaaS or platform context. 
• Hands-on experience with large-scale production ML systems: serving infrastructure, monitoring pipelines, and retraining workflows.
• Familiarity with causal inference methods and A/B experimentation frameworks at scale.
• Experience applying LLMs, RAG, or modern NLP techniques in real-world product contexts (forward-looking for this role; not a near-term expectation).
• Contributions to team best practices, onboarding materials, or internal scientific community (brownbags, code reviews, method clinics).
• Experience mentoring junior scientists or analysts toward applied science career paths, or leading cross-functional technical projects.