{"id":3627793,"url":"https://alion.io/job/sperry-rail-full-stack-engineer-data-science-focus","title":"Full Stack Engineer- Data Science Focus","company":{"id":4495570,"name":"Sperry Rail","domain":"sperryrail.com","url":"https://alion.io/company/sperry-rail","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"B","score":75,"open_postings":23,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Derby, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Amazon ECS","optional":true},{"name":"AWS Fargate","optional":true},{"name":"CloudFormation","optional":true},{"name":"Docker","optional":true},{"name":"JavaScript","optional":true},{"name":"React.js","optional":true},{"name":"Terraform","optional":true},{"name":"Time Series","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-08-20T21:02:24Z","employer_posted_date":"2026-08-20","last_verified_at":"2026-11-05T10:23:01Z","board_verified":true,"closed_at":null,"days_open":76,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":76},"description":"About Sperry:\nSperry Rail is on a mission-critical journey to revolutionize the Rail Flaw Detection industry. Through the continuous development of cutting-edge diagnostic technologies and AI-assisted analysis, we are transforming railway safety worldwide. Our global engineering teams work collaboratively to develop step-change technologies that define Sperry as the unparalleled market leader.\nFor nearly a century, we have repeatedly modernized and improved rail diagnostics through our relentless pursuit of improvement. Determined is an understatement. We are obsessed with advancing science and raising the bar on what’s possible with our ever-improving suite of products and service offerings.\nEmboldened through the shared values of honesty, accountability, passion, integrity, and teamwork, we are driven by the challenge and bridging concepts with fruition. Each technologist entering Sperry imprints themselves into our brand and further galvanizes a culture of innovation and advancement. Allow us to be clear, Thought Leaders are welcome!\nWe are agile and hungry and invite those with similar passions to join us in challenging the status quo and bringing new ideas to the market. Fast-paced, high-touch with a distinct sense of purpose. We offer more than a job; we offer an opportunity to be part of something different.\nRole Summary\nWe are looking for Full Stack Engineers with a data science focus to design, build, and maintain the data-driven applications and pipelines behind Zula 2.0, the platform that ranks and fuses the output of our rail inspection systems. You will work across the full stack with a strong emphasis on backend data processing, analytics, and machine learning workflows, all hosted on AWS cloud infrastructure. These seats sit close to the ranking and fusion logic itself, so the work is as much about understanding what the system should surface and in what order as it is about serving it. Zula 2.0 is the critical path for the team through the end of the year.\nWhat We Expect From You\nWe expect an exceptional level of drive and ambition. You think beyond today's work to what the team and organisation need next, champion bold ideas, and see them through. Your hunger is infectious - it inspires those around you to aim higher. You should be someone who puts the team first. You share credit openly, admit when you are wrong, and welcome feedback as an opportunity to grow. You are comfortable saying \"I don't know\" and asking for help when needed. Strong interpersonal skills are important for this role. You should have good instincts for working with different people, listen actively, navigate disagreements constructively, and communicate clearly with both technical and non-technical audiences. This role requires a high degree of self-direction. You will manage complex work with minimal oversight, identify problems and solutions proactively, and may lead workstreams or small teams. You make well-reasoned technical decisions and only escalate when there is genuine business or architectural impact. Strong analytical thinking is critical. You will need to decompose ambiguous, multi-faceted problems, reason about complex distributed systems and data flows, and anticipate the downstream consequences of design decisions. You translate business problems into elegant technical solutions.\nKey Responsibilities\nDesign and develop data pipelines, ETL processes, and backend services using Python\nBuild and maintain APIs and microservices that serve data products to internal and external consumers\nWork with large-scale rail inspection datasets including ultrasonic, electromagnetic, and operational data\nImplement and deploy machine learning models and statistical analyses in production environments\nArchitect and manage AWS cloud infrastructure for data workloads (S3, Lambda, Glue, Athena, SageMaker, Step Functions)\nCollaborate with domain experts to translate rail inspection requirements into technical solutions\nWrite clean, tested, well-documented code following engineering best practices\nParticipate in code reviews, sprint planning, and technical design discussions\nCollaborate with cross-functional teams including inspection engineers, data scientists, and product managers\nContribute to a culture of continuous improvement, knowledge sharing, and technical excellence\nRequired Skills & Qualifications\nStrong proficiency in Python (NumPy, Pandas, Scikit-learn, or similar)\nExperience building data pipelines and working with structured and unstructured data\nSolid understanding of SQL and relational and non-relational databases\nExperience with AWS cloud services (S3, Lambda, EC2, RDS, Glue, or similar)\nStrong applied mathematics - statistics, probability, linear algebra, or signal processing\nDemonstrated ability to read, understand, and extend complex algorithms and dense business logic written by others\nUnderstanding of software engineering principles (testing, code quality, design patterns)\nFamiliarity with version control (Git), CI/CD pipelines, and agile development practices\nStrong problem-solving skills and ability to learn new technologies quickly\nGood communication skills - able to explain technical concepts to non-technical stakeholders\nA collaborative, team-first mindset aligned with our values of being Humble, Hungry, and Smart\nDesirable Skills\nExposure to time-series data, geospatial data, or IoT data streams\nExperience with ranking, scoring, or data fusion systems\nFamiliarity with frontend technologies (React, TypeScript)\nExperience in rail testing, NDT, or sensor-based inspection industries (ultrasound, eddy current, electromagnetic, etc.)\nAWS certifications\nExperience with infrastructure-as-code (CloudFormation, CDK, Terraform)\nKnowledge of containerisation (Docker, 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