{"id":4187750,"url":"https://alion.io/job/ey-senior-applied-optimisation-scientist","title":"Senior Applied Optimisation Scientist","company":{"id":2859,"name":"EY","domain":"ey.com","url":"https://alion.io/company/ey-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Science & Research","role_family":"Science & Research","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":".NET","optional":true},{"name":"C#","optional":true}],"status":"live","first_seen_at":"2026-11-12T18:35:10Z","employer_posted_date":null,"last_verified_at":"2026-11-12T18:35:10Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"The Opportunity\n\nWe are looking for a highly skilled Applied Optimisation Scientist to design, extend, and validate large-scale mathematical optimisation models supporting strategic mine planning and optimisation platforms. This role focuses on solving complex, high-impact planning problems using advanced optimisation techniques to drive long-term operational and investment decisions. You will work at the intersection of mathematical modelling, software engineering, and domain-specific problem-solving, contributing to mission-critical systems that directly influence business performance. The role requires strong expertise in linear and mixed-integer optimisation, hands-on solver experience, and the ability to translate real-world constraints into scalable optimisation solutions.\n\nYour Key Responsibilities:\nDevelop and maintain large-scale LP/MIP optimisation models for mine planning and scheduling.\nDesign and implement constraints including capacity, sequencing, blending, cost optimisation, and boundary conditions.\nSupport advanced optimisation workflows such as Direct Block Scheduling (DBS).\nContinuously improve model structure and performance for production-grade deployments.\nIntegrate and configure optimisation solvers such as Gurobi, Maroma, and AMPL.\nEnsure high performance, scalability, and numerical stability of models.\nDiagnose and resolve infeasible solutions, performance bottlenecks, and instability issues.\nOptimize solver parameters and execution strategies for efficiency.\nValidate optimisation outputs through rigorous testing, cross-validation, and alternate formulations.\nEnsure correctness, reliability, and consistency of outputs.\nTake ownership of solution quality and defend results with strong analytical reasoning.\nCollaborate with product teams, engineers, and domain experts to translate business problems into mathematical models.\nCommunicate optimisation assumptions, trade-offs, and outcomes to both technical and non-technical stakeholders.\nSupport decision-making by providing insights derived from optimisation models.\n\nRequired Skills:\nStrong background in Operations Research, Applied Mathematics, and optimisation techniques.\nProven experience in Linear Programming (LP) and Mixed Integer Programming (MIP).\nHands-on experience with Gurobi, Maroma, and AMPL (mandatory).\nExperience with additional solvers such as CPLEX or Xpress is advantageous.\nStrong Python programming skills for model development, integration, and testing.\nExperience handling large-scale, performance-critical datasets.\nSolid understanding of optimisation model performance tuning and stability considerations.\n\nPreferred Skills & Experience:\nExperience in mine planning, supply chain optimisation, or heavy industry use cases.\nExposure to graph-based optimisation and network flow models.\nFamiliarity with enterprise-scale optimisation systems.\nWorking knowledge of C# / .NET (preferred).\n\nCore Technology Stack:\nOptimisation Solvers: Gurobi, Maroma, AMPL\nMethods: Linear Programming, Mixed Integer Programming, Network Flow Optimisation, Constraint Modelling\nProgramming: Python, AMPL, C#/.NET (preferred)\n\nKey Competencies:\nStrong analytical and structured problem-solving skills\nAbility to solve complex real-world optimisation challenges\nOwnership mindset with strong attention to detail\nEffective communication and stakeholder engagement\nAbility to balance model accuracy with computational efficiency\n\nEducation:\nBachelor's or master's degree in operations research, Mathematics, Engineering, Computer Science, or related field.\nSkills\nAMPL, Gurobi, Maroma, Optimisation, Optimization, Python","description_format":"text","description_chars":3628,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Audit & Assurance Firms","Financial Advisory, Valuation & Restructuring","Management Consulting","Tax Advisory & Preparation"],"lifecycle":[{"event":"open","at":"2026-11-12T18:35:10Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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