{"id":1217997,"url":"https://alion.io/job/maya-staff-research-scientist","title":"Staff Research Scientist","company":{"id":50036,"name":"Maya","domain":"maya.ph","url":"https://alion.io/company/maya-ph","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Metro Gate Indang, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":31000,"max_usd":70000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1148},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Speech Recognition","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-09-05T16:00:00Z","employer_posted_date":"2026-09-05","last_verified_at":"2026-10-15T23:05:55Z","board_verified":true,"closed_at":null,"days_open":40,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":40},"description":"What you will be doing:\nLead applied AI research and develop innovative machine learning, optimization, and data-driven solutions across Maya's AI initiatives.\nTranslate complex business problems into production-ready AI, machine learning, and optimization solutions.\nDesign, prototype, validate, and deploy scalable models and systems that deliver measurable business impact.\nDrive the adoption of emerging AI technologies and research advancements, including Causal AI and Neuro-symbolic AI.\nPartner closely with Product, Engineering, and Business teams to bring research innovations into production.\nLead and grow a high-performing AI research team, providing technical direction, mentorship, and strategic leadership.\nDefine and execute research roadmaps aligned with business priorities and Maya's AI strategy.\nRepresent Maya in the AI research community through academic partnerships, publications, and participation in leading AI/ML conferences.\nDrive improvements across lending, fraud, marketing, and intelligent operations through innovative AI solutions, faster deployment of research, and increased adoption of frontier AI techniques.\nWhat we are looking for:\nMaster's degree or PhD in Statistics, Mathematics, Computer Science, or a related quantitative field.\nExperience in Data Science, Machine Learning, AI Research, or a related field.\nExperience leading and managing high-performing Data Science teams.\nProven track record of delivering impactful AI or Data Science solutions in fast-paced environments.\nStrong analytical and problem-solving skills with the ability to design innovative solutions while balancing business, regulatory, and responsible AI requirements.\nPublication record or demonstrated research contributions through conference papers, workshops, or pre-prints is an advantage.\nDisplayed Skill Mastery:\nProven leadership, people management, and stakeholder management skills.\nAbility to lead, mentor, and develop high-performing teams.\nStrong communication, presentation, and interpersonal skills.\nExperience working in agile environments and managing complex projects.\nAbility to balance hands-on technical contributions with leadership responsibilities across multiple AI research domains.\nSpecialization Track A: LLMs, Audio Processing & Speech AI\nExperience developing and fine-tuning speech, audio, and language models in research or production environments.\nStrong knowledge of speech recognition, speaker verification, emotion recognition, audio classification, and speech-based behavioral modeling.\nExperience with transformer-based architectures, multimodal learning, self-supervised learning, and representation learning.\nStrong proficiency in PyTorch, TensorFlow, digital signal processing, feature engineering, and deep learning architectures such as Transformers, CNNs, and RNNs.\nFamiliarity with Causal AI and Neuro-symbolic AI approaches.\nBackground in behavioral science, psychology, psychometrics, or behavioral modeling is a plus.\nSpecialization Track B: Operations Research & Optimization\nStrong background in Operations Research, Optimization, or Applied Mathematics.\nExperience solving complex optimization problems, including linear programming, mixed-integer programming, multi-objective optimization, scheduling, routing, and resource allocation.\nExperience developing algorithms, heuristics, simulation models, and decision-support systems.\nProficiency with optimization tools such as Gurobi, CPLEX, OR-Tools, SCIP, or similar platforms.\nStrong understanding of mathematical modeling, algorithms, and computational 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