We are seeking a “Senior Data Scientist” with 4 to 5 years of experience who views "Data Scientist" not just as a job title, but as a mission to drive radical organizational change. We don't need a researcher who stays in the lab; we need an “Action-Oriented Architect of Value”. 

You are someone who possesses "immense energy” the kind that transforms a stagnant project into a high-velocity success story. You believe that every line of code, every data cleaning step, and every model iteration is an opportunity to create tangible business value. You balance this high-octane energy with the “meticulousness of a perfectionist”, ensuring that while we move fast, we never compromise on the integrity or precision of our insights. 

Value‑First Data Science Delivery: Drive data science initiatives that prioritise measurable business impact over exploratory insights. Clearly define the value hypothesis, success metrics, and decision pathways before execution, ensuring every model, feature, and analysis directly contributes to operational efficiency, quality improvement, cost reduction, or revenue growth.

End‑to‑End Ownership of Data Products: Take full ownership of data science solutions across their lifecycle—from data exploration and feature engineering through model development, validation, deployment, and post‑deployment monitoring. Ensure models are reliable, explainable, scalable, and aligned with real‑world constraints.

Meticulous Engineering & Code Quality: Apply a perfectionist mindset to data pipelines, feature stores, and model code. Build modular, reusable, and well‑documented solutions that meet production engineering standards, with strong emphasis on reproducibility, versioning, testing, and maintainability.

Rapid Deployment & Iterative Execution: Operate with a strong “Day One” bias for action. Accelerate the transition from ideation to deployment by delivering incrementally, validating assumptions early, and continuously improving models based on feedback and observed performance in production.

Statistical & Methodological Rigor: Apply sound statistical principles and validation techniques to ensure insights are robust and defensible. Proactively identify data quality issues, bias, leakage, and edge cases, and address them to protect the integrity of conclusions and model outputs.

Advanced Machine Learning & AI Application: Leverage advanced ML techniques, deep learning frameworks, and emerging Generative AI/LLM capabilities where they deliver tangible business value. Stay current with evolving methodologies and assess their applicability in an industrial and manufacturing context.

Executive‑Level Storytelling: Translate complex analytical results into clear, concise, and compelling narratives for senior leadership. Communicate insights in a way that drives confident decision‑making, balancing technical depth with business clarity.

Cross‑Functional Collaboration: Act as a catalyst across engineering, product, manufacturing, quality, and operations teams. Embed data‑driven thinking into workflows and decision processes, ensuring alignment between business needs and technical solutions.

Operational Excellence & Continuous Improvement: Monitor deployed models for performance, drift, and reliability. Own continuous improvement cycles, ensuring solutions remain accurate, relevant, and impactful as data, processes, and business priorities evolve.

• Immense Energy: You are the person who motivates the room. You bring a "can-do" spirit to complex legacy data problems. 

• Action to Change: You aren't satisfied with the status quo. If a process is broken, you don't just complain—you build the solution to fix it. 

• Meticulousness of Perfection: You have a "zero-defect" mindset regarding data quality. You catch the edge cases that others miss because you care about the "last 1%" of accuracy. 

• Lifelong Learner: You treat your career like a high-growth startup, constantly upskilling and staying at the bleeding edge of Arxiv papers and new methodologies. 

As a Data Scientist at Agratas, you will play a critical role in transforming data into measurable business value across manufacturing, quality, and operations. This role goes beyond analysis—focusing on building, deploying, and scaling production‑ready data science solutions that influence real‑world decisions. You will work closely with cross‑functional teams to embed advanced analytics and AI into the core of the organisation, helping shape a data‑driven culture in a fast‑growing industrial environment.

• Experience: 4–5 years in professional Data Science roles, with a proven track record of moving models into live production environments. 

• Technical Excellence: Mastery of Python/R and the data science stack (Pandas, Scikit-learn, XGBoost/LightGBM). Deep expertise in SQL and working with large-scale distributed data sets (Spark, Snowflake, or BigQuery). 

• Statistical Rigor: A perfectionist approach to statistics. You understand the "why" behind the "how," ensuring that our conclusions are scientifically sound and not just "noise." 

• Advanced AI Knowledge: Strong familiarity with Deep Learning frameworks and an obsessive interest in how Generative AI and LLMs can be tuned to create specific business value.