Applix

Applix provides an AI-powered manufacturing ecosystem designed to achieve zero defects, zero waste, and zero delays. Their platform offers real-time AI-driven defect detection, root cause analysis,…

AIInventory ManagementManufacturingQuality ControlSupply ChainZero Defectsapplix.ai

Applix

AIInventory ManagementManufacturingQuality ControlSupply ChainZero Defectsapplix.ai

HQAustin, US

Team Size14

Open Jobs33

Total Funding-

Latest FundraiseUnknown

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Data Scientist (Machine Learning & Production Systems)

On-SiteGreater Bengaluru Area, IN

On-Site • Greater Bengaluru Area, IN

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Company Description

Applix offers the industry’s only Manufacturing Operating System, designed to provide automation, control, and scalability tailored to the unique needs of modern factories. Focused on delivering smarter solutions, Applix empowers manufacturers to optimize operations and achieve operational efficiency.

We work directly with global enterprises inside real production and business environments, combining data, AI, and workflows to drive zero defects, zero waste, and zero delay.

Role Description

This is a full-time, on-site role for a Data Scientist (Machine Learning & Production Systems) based in Bangalore.

You will be responsible for designing, developing, deploying, and monitoring machine learning systems that directly impact procurement, operations, and enterprise decision-making. The role involves working across the full lifecycle — from data pipelines and modeling to production deployment, orchestration, monitoring, and business adoption.

You will analyze complex datasets, build predictive models, and develop decision-support tools while collaborating with cross-functional teams across business, engineering, and analytics to deliver scalable, production-ready solutions. This role requires strong ownership, technical depth, and the ability to translate data into real business outcomes.

Strong candidates should be comfortable working not only on model development, but also on deployment pipelines, CI/CD workflows, orchestration systems, and production monitoring of ML systems.

Qualifications

Strong experience in Data Science, Machine Learning, and Statistical Modeling (e.g., time series, clustering, tree-based models, regression, or neural networks)

Proficiency in Python for model development and data analysis

Strong SQL skills with experience working on large-scale datasets (Snowflake or similar platforms preferred)

Experience building and maintaining data pipelines (ETL/ELT) and analytics-ready datasets

Experience deploying and monitoring machine learning models in production environments

Familiarity with MLOps practices, including CI/CD pipelines, model versioning, deployment workflows, rollback strategies, and automated retraining pipelines

Experience with orchestration and workflow management tools such as Airflow, Prefect, Dagster, or similar platforms

Understanding of production ML monitoring concepts including data drift, model drift, performance degradation, alerting, and observability

Experience working with cloud platforms and ML infrastructure (AWS, Databricks, SageMaker, Docker, Kubernetes, MLflow, or similar tools)

Strong understanding of data modeling, data quality, and data governance principles

Experience with data visualization tools (e.g., Power BI) to build dashboards and decision-support tools

Experience working with Large Language Models (LLMs), RAG systems, vector databases, or NLP technologies is a strong plus

Strong analytical and problem-solving skills with the ability to work cross-functionally

Bachelor’s or higher degree in Computer Science, Statistics, Mathematics, Engineering, or a related field

Experience in manufacturing, procurement, supply chain, or enterprise analytics environments is an advantage