{"id":1238327,"url":"https://alion.io/job/range-senior-machine-learning-engineer-data-analyst-financial-risk-scoring","title":"Senior Machine Learning Engineer & Data Analyst – Financial Risk Scoring","company":{"id":2047822,"name":"Range","domain":"range.org","url":"https://alion.io/company/range-org","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Deel","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":75000,"max":130000,"currency":"USD","period":"year","gross":null,"usd_annual":130000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Feature Store","optional":true},{"name":"Flink","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-04-27T18:24:12Z","employer_posted_date":"2026-04-27","last_verified_at":"2026-09-25T23:57:51Z","board_verified":true,"closed_at":null,"days_open":152,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":152},"description":"We are looking for a senior machine learning expert and data analyst to help us design, extend, and operate financial risk scoring systems at scale. You’ll work on models and pipelines that process hundreds of terabytes of data and power decisions where accuracy, explainability, and robustness matter. This role sits at the intersection of machine learning, fintech analytics, and big-data engineering. You’ll help evolve our scoring algorithms, improve signal quality, and ensure our models remain reliable and interpretable in production environments. We’re especially interested in someone who can combine strong ML theory, hands-on data engineering, and pragmatic fintech experience.\nWhat You’ll Do\nDesign and improve financial risk scoring algorithms and models.\n\nAnalyze large-scale datasets (hundreds of TBs in Elasticsearch and related systems).\n\nBuild and maintain data processing pipelines for feature generation, training, and evaluation.\n\nDevelop ML models for anomaly detection, fraud detection, credit/risk scoring, and behavioral analysis.\n\nValidate models for accuracy, bias, stability, and drift over time.\n\nEnsure models are explainable, auditable, and production-ready.\n\nWork closely with engineering teams to deploy models into production systems.\n\nOptimize performance and cost across large-scale data infrastructure.\n\nDefine metrics, dashboards, and monitoring for model performance.\n\nInvestigate edge cases and failure modes in scoring systems.\n\nWhat We’re Looking For\nMust-have\nSenior-level experience in machine learning and data analysis. (5+ years)\n\nStrong background in financial risk, fintech analytics, or fraud detection.\n\nExperience building and deploying production ML models.\n\nStrong Python ecosystem skills (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, etc.).\n\nExperience with large-scale data processing (100s of TBs).\n\nDeep experience with Elasticsearch or similar distributed data stores.\n\nExperience designing data pipelines (batch and/or streaming).\n\nStrong statistical reasoning and experimentation skills.\n\nAbility to translate business risk concepts into measurable model features.\n\nExperience evaluating model drift, bias, and long-term stability.\n\nNice-to-have\nExperience with real-time scoring systems.\n\nExperience with distributed compute frameworks (Spark, Beam, Flink, etc.).\n\nFamiliarity with regulatory or compliance-sensitive environments.\n\nExperience with graph-based risk models or transaction network analysis.\n\nExperience building internal analytics tools or dashboards.\n\nKnowledge of feature stores and model versioning systems.\n\nHow You Work\nYou think critically about data quality and signal reliability.\n\nYou design models that are robust, explainable, and production-safe.\n\nYou’re comfortable moving between analysis, modeling, and infrastructure.\n\nYou can handle messy, real-world financial data at scale.\n\nYou communicate clearly with engineers, product teams, and stakeholders.\n\nYou care about correctness and long-term maintainability.\n\nExample Problems You Might Work On\nExtending risk scoring models with new behavioral signals.\n\nDetecting anomalous transaction patterns across massive datasets.\n\nImproving precision/recall tradeoffs in production scoring.\n\nBuilding pipelines that process and index large transaction datasets.\n\nDesigning model monitoring to detect drift and degradation.\n\nOptimizing large-scale Elasticsearch queries and aggregations.\n\nCombining rule-based and ML-based scoring systems.\n\nWhy Join Us\nCompetitive salary + performance incentives\n\nEquity aligned with long-term growth\n\nHigh ownership and direct exposure to leadership\n\nRemote-first with global team\n\nHealth and sports benefits\n\nYearly international team off-sites\n\nWork on high-impact financial risk systems.\n\nTackle real-world ML challenges at large scale.\n\nInfluence the architecture of our scoring and analytics platform.\n\nCollaborate with experienced engineers and data specialists.\n\nOwn meaningful parts of our data and ML strategy.\n\nHow to Apply\nSend us:\nA short introduction and relevant experience.\n\nExamples of ML or risk models you’ve worked on.\n\nLinks to projects, papers, or code (if available).\n\nWe’re particularly interested in candidates who can demonstrate experience building robust financial risk models on very large datasets and bringing them successfully into production.","description_format":"text","description_chars":4346,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Blockchain & Crypto","Cryptocurrencies","Stablecoins"],"lifecycle":[{"event":"open","at":"2026-09-25T16:23:32Z"}],"liveness":{"score":5,"band":"cold","label":"Long shot","p_open":1,"p_active":0.176,"p_room":0.28,"age_days":151,"expected_fill_days":30,"reasons":["conf:5","win:tail","crowd:"],"computed_at":"2026-09-26T05:45:00Z"},"pay":{"stated_usd_annual":130000,"is_top_pay":false},"html_url":"https://alion.io/job/range-senior-machine-learning-engineer-data-analyst-financial-risk-scoring","json_url":"https://alion.io/job/range-senior-machine-learning-engineer-data-analyst-financial-risk-scoring.json","meta":{"generated_at":"2026-09-27T00:47:40Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":711,"day_limit":5000,"remaining_today":4289,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}