# Data Scientist at Jobgether > Remote · India · Unspecified · Analytics / Data science · open. Source: https://alderhq.app/jobs/lever%3Ajobgether%3Ac46d1944-e477-4cc9-bbd4-0cf24820d63e (read 2026-11-05). ## Facts (observed) - Company: [Jobgether](https://alderhq.app/companies/jobgether) - Location text: India - Where it can be done from (inferred): Remote · India (evidence: "Location lists "india" and remote") - Posted: 2026-11-04 (1 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.lever.co/jobgether/c46d1944-e477-4cc9-bbd4-0cf24820d63e - Skills listed (inferred): Python, AWS, Azure, SQL, LLMs ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (India, n 3): p25 $52k, median $74k, p75 $146k ## Description (the employer's text) ### Accountabilities In this role, you will design, develop, and operationalize data science solutions that improve quality, food safety, manufacturing efficiency, and supply chain performance. Your responsibilities will include: Develop predictive models for product quality outcomes, critical quality parameters, supplier risks, defect probabilities, non-conformance risks, process deviations, and first-pass yield across multiple product categories. Build audit-score prediction models to anticipate audit outcomes, identify potential findings, assess site-level quality risks, and prioritize preventive actions. Apply NLP, clustering, causal analysis, and machine learning to classify non-conformances, identify recurring root causes, predict recurrence, and evaluate corrective and preventive action (CAPA) effectiveness. Develop analytics for food safety hazards, hygienic zoning, pathogen monitoring, environmental monitoring, sanitation, allergen control, and cross-contamination risks. Create calibration and measurement-system analytics to identify overdue calibration risks, instrument drift, measurement uncertainty, and compliance gaps. Use NLP and Generative AI to extract insights from audit reports, CAPA narratives, consumer complaints, standard operating procedures, food safety plans, maintenance logs, and supplier communications. Build end-to-end quality analytics connecting supplier performance, incoming material risks, and customer quality outcomes. Develop dashboards, risk scorecards, alerts, and self-service analytics for Quality, Food Safety, Manufacturing, and Supply Chain stakeholders. Build Industrial Internet of Things (IIoT)-enabled analytics using data from PLCs, SCADA systems, historians, MES platforms, sensors, edge devices, vision systems, LIMS, and quality management platforms. Develop predictive maintenance models covering equipment failures, asset health, remaining useful life, anomaly detection, and maintenance prioritization. Support automated process control through process modeling, deviation prediction, setpoint recommendations, and feedback-driven control strategies. Apply statistical process control, process capability analysis (CpK/PpK), design of experiments, and structured root-cause methodologies such as 8D, 5-Why, and FMEA. Develop optimization and simulation models for production scheduling, line balancing, capacity planning, material flows, quality resource allocation, sanitation scheduling, and calibration planning. Support the deployment and industrialization of data science models across cloud, edge, and on-premise environments, ensuring monitoring, retraining, governance, and auditability. Collaborate with Quality and Food Safety, Manufacturing, Research and Development, Engineering, Maintenance, Procurement, Supply Chain Excellence, and Digital Services teams to identify high-impact use cases and define measurable success criteria. Design, develop, validate, deploy, and continuously improve machine learning, statistical, NLP, computer vision, optimization, simulation, and Generative AI solutions using Databricks and Azure and/or AWS. Build scalable, secure, and governed cloud data architectures in collaboration with AI, Data, and Analytics Platform teams. Integrate data from SAP S/4HANA, SAP ECC, SAP EWM, MES, o9, IMS, QMS/eQMS, food safety systems, audit tools, and IIoT platforms into reliable data products. Develop real-time dashboards, alerts, scorecards, and self-service reporting using Tableau, Power BI, Databricks SQL, and Python-based tools. Implement MLOps practices covering version control, CI/CD, model monitoring, drift detection, retraining, security, governance, and auditability. Measure and communicate business impact through quality, food safety, manufacturing, and supply chain KPIs. ### Requirements The ideal candidate is an experienced data science professional with strong cloud engineering capabilities and a background in manufacturing, FMCG, quality analytics, or industrial AI. You should bring: 5–8+ years of experience in quality analytics, food safety analytics, manufacturing analytics, supply chain analytics, industrial AI, or data science within an operational or FMCG environment. ### How Jobgether works We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/lever%3Ajobgether%3Ac46d1944-e477-4cc9-bbd4-0cf24820d63e and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.