{"id":1491826,"url":"https://alion.io/job/csc-generation-senior-machine-learning-engineer-causal-decision-systems-4","title":"Senior Machine Learning Engineer, Causal & Decision Systems","company":{"id":8524,"name":"CSC Generation","domain":"cscgeneration.com","url":"https://alion.io/company/csc-generation","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":77,"open_postings":158,"ghost_share":0,"stale_share":0.924,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Costa Rica"],"countries":["CR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":30000,"max_usd":76000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1507},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"bandit","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-29T20:22:45Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-01T15:54:31Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.\nReports to: CTO\nLocation: Hybrid - Costa Rica\nAbout the Role\nCSC Generation is building closed-loop decision systems that use machine learning to operate consumer businesses more intelligently. We are starting with pricing and expanding into areas such as inventory, purchasing, promotions, marketing, and assortment.\nYou will help build systems that estimate causal response and quantify uncertainty, choose actions, generate useful information, observe outcomes, update policies, evaluate challengers, and deploy within guardrails.\nWe want to answer questions such as:\nWhat happens because we change a price, rather than simply what happens next?\nHow should uncertainty affect a decision?\nWhen should the system exploit what it knows versus experiment to learn?\nCan we estimate the value of a challenger policy before fully deploying it?\nHow do we optimize economic outcomes while respecting inventory, margin, vendor, customer, and operational constraints?\nWhat You'll Do\nDepending on your background, you may work across:\nCausal and heterogeneous treatment-effect modeling\nUncertainty estimation and calibration\nContextual bandits, active learning, or sequential decision-making\nPolicy learning and constrained optimization\nCounterfactual and off-policy evaluation\nExperimentation and champion/challenger systems\nProduction ML infrastructure, monitoring, and automated deployment\nWe care about selecting the right method, not using a particular framework.\nWhat Success Looks Like\nSuccess is not a better offline metric.\nThe systems you build should produce measurable economic lift in controlled experiments, generalize across businesses, learn from their own interventions, and safely automate an increasing share of real commercial decisions.\nOver time, the goal is simple:\nthe system should become better at operating the business because it has operated the business.\nWhat We're Looking For\nWe care more about exceptional technical ability and judgment than matching a checklist. Strong candidates will have experience in several of:\nMachine learning and statistical modeling\nCausal inference and experimentation\nRecommendation, advertising, pricing, marketplace, credit, or other decision systems\nBandits, reinforcement learning, optimization, or active learning\nUncertainty estimation\nCounterfactual evaluation\nProduction ML systems\nPython, SQL, and large behavioral datasets\nWhy This Role Is Different\nMost ML systems learn from a dataset. Here, the decisions made by the model influence the data the model sees next. That creates a continuous loop: decision, intervention, outcome, learning, better decision.\nThe long-term opportunity is to build that capability once and apply it across a portfolio of businesses and increasingly broad commercial decisions.\nReal-world impact. The systems you build will run live commercial decisions across a portfolio of consumer brands, so you will see measurable economic outcomes from your work, not just offline benchmark improvements.\nTechnical growth at the frontier. Causal decision systems that learn from their own interventions are still an open problem. You will work at the intersection of causal ML, bandit algorithms, and production engineering, with the latitude to choose the right method for the problem.\nFull ownership. You will own problems end to end, from framing and modeling through production deployment and evaluation.\nCompetitive benefits. We offer an attractive benefits package including primarily remote work, private medical and life insurance, additional paid time off, monthly allowances and reimbursements, employee discounts, and opportunities for professional growth.\nInterview Process\nRecruiter Screen: A conversation with our recruiting team to cover your background, the role, and mutual fit.\nVirtual Interview Rounds: Focused discussion with the hiring manager & deeper conversations with cross-functional engineering and data science collaborators covering technical depth, system design, and working style.\nIn-Person Interview: A final on-site visit at our Costa Rica office to meet the broader team and connect with key stakeholders.\nReference Checks: Conducted in parallel with the final stages where possible.\nOffer: We move quickly for the right candidate.\nInterview process is subject to change. Any updates will be shared promptly and clearly.\nPlease Note\nPart of our interview process is a mandatory in-person interview with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately. \nDue to a high volume of fraudulent applications, you must share a valid LinkedIn profile URL in the application questions below to be considered. If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.","description_format":"text","description_chars":5385,"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":["Life insurance"],"hiring_locations":[{"name":"Costa Rica","iso":"CR","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["E-commerce Platforms","Retail & Shop Systems"],"lifecycle":[{"event":"open","at":"2026-09-30T00:53:43Z"}],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":1,"expected_fill_days":36,"reasons":["conf:6","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/csc-generation-senior-machine-learning-engineer-causal-decision-systems-4","json_url":"https://alion.io/job/csc-generation-senior-machine-learning-engineer-causal-decision-systems-4.json","meta":{"generated_at":"2026-10-02T03:12:05Z","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":4384,"day_limit":5000,"remaining_today":616,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}