{"id":1021069,"url":"https://alion.io/job/qualcomm-staff-data-scientist","title":"Staff Data Scientist","company":{"id":94,"name":"Qualcomm","domain":"qualcomm.com","url":"https://alion.io/company/qualcomm","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":70,"open_postings":1059,"ghost_share":0.25,"stale_share":0.146,"repost_share":0.449,"time_to_fill_p50_days":74,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":34000,"max_usd":63000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":17},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Feature Store","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest-Assured","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-04-29T00:00:00Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-11-07T10:09:52Z","board_verified":true,"closed_at":null,"days_open":192,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":192},"description":"Company:\nQualcomm India Private LimitedJob Area:\nInformation Technology Group, Information Technology Group > Data Science General Summary:\nThe Staff Data Scientist / Full Stack Developer is a senior, hands on individual contributor responsible for designing, building, and operationalizing traditional machine learning models, agentic AI systems, and Databricks native data applications that drive real business outcomes. This role operates at the intersection of data science, ML engineering, and full stack data application development, with a strong focus on production grade solutions. This position requires deep expertise with AI assisted development of classic ML techniques, agent based AI workflows, and Databricks application development, along with strong ownership of end to end delivery-from data preparation and modeling to deployment, monitoring, and user facing experiences.\nKey Responsibilities\nTraditional Machine Learning & Analytics\nDesign, develop, and deploy traditional machine learning models, including regression, classification, clustering, time series forecasting, and anomaly detection.\nPerform feature engineering, model selection, training, validation, and performance tuning on large scale enterprise datasets.\nApply sound statistical and ML best practices to ensure model robustness, explainability, and business relevance.\nAgentic AI & Intelligent Automation\nDesign and implement agentic AI workflows, where autonomous or semi autonomous agents orchestrate data access, ML inference, decision logic, and actions.\nBuild multi step agent pipelines that combine rules, ML models, and reasoning components to solve complex business problems.\nIntegrate agentic systems with enterprise data, ML models, and applications to enable intelligent automation and decision support.\nDatabricks Application Development\nDesign and develop Databricks native applications, including notebook based apps, interactive dashboards, and parameterized data/ML workflows.\nBuild data and ML services/APIs leveraging Databricks, Python, and Lakehouse capabilities.\nPartner with analytics, BI, and application teams to embed ML insights, predictions, and agent outputs directly into Databricks apps and business workflows.\nEnsure Databricks apps meet performance, security, governance, and usability standards.\nML Engineering & Productionization\nOperationalize ML models and agentic workflows into production pipelines, ensuring scalability, reliability, and monitoring.\nCollaborate with data engineering teams to leverage curated Lakehouse data, feature stores, and governed datasets.\nImplement model monitoring, drift detection, and retraining strategies to maintain long term model effectiveness.\nFull Stack Data Enablement\nDevelop end to end solutions that span data ingestion, modeling, ML inference, agent execution, and user facing applications.\nTranslate business and analytical requirements into scalable, maintainable ML powered data products.\nEnable downstream consumption through Databricks apps, dashboards, APIs, and integrated enterprise applications.\nProduction Support & Operational Excellence\nOwn production ML models, agentic systems, and Databricks applications, including monitoring, troubleshooting, and root cause analysis.\nImplement logging, alerting, and observability for models, agents, and applications.\nDrive continuous improvements in model accuracy, system reliability, and user experience.\nTechnical Leadership & Influence\nServe as a technical authority in traditional ML, agentic AI, and Databricks application patterns.\nInfluence architectural decisions, best practices, and technical standards across teams.\nMentor peers and raise the bar on ML rigor, engineering quality, and production readiness.\n10+ years of hands on experience in data science, applied machine learning, or ML engineering, with ownership of production systems.\n5+ years of experience in building RAG based GenAI agentic applications and workflows\nStrong proficiency in Python for ML development, data processing, and application logic.\nDeep experience with traditional ML techniques (e.g., regression, classification, clustering, time series).\nProven experience building and deploying ML models in production environments.\nHands on experience with Databricks, including Databricks application development (notebooks, workflows, dashboards, ML pipelines).\nStrong understanding of feature engineering, model evaluation, and explainability.\nExperience collaborating with data engineering, BI, and application teams.\nBachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.\nFamiliarity with Lakehouse architectures, feature stores, and ML lifecycle management.\nExperience with MLOps practices, CI/CD, model monitoring, and retraining pipelines.\nExposure to cloud platforms (e.g., AWS) and scalable ML infrastructure.\nExperience embedding ML and agent outputs into enterprise applications or analytics platforms.\nKnowledge of data governance, access controls, and secure ML deployment.\n Minimum Qualifications:\n• Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.5+ years of Data Science or related work experience.\n*Completed advanced degrees in a relevant field may be substituted for up to two years (Master’s = one year, Doctorate = two years) of work experience.\nApplicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail  or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).\nQualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.\nTo all Staffing and Recruiting Agencies:Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. 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