Location : Gurugram (5days, Work from Office)

Industry Preference : Candidates must be from the manufacturing, automotive, industrial or asset-heavy sector. Candidates from consulting firm will also be considered, provided they are currently handling manufacturing clients.

Education & Experience :

- Bachelor's / Master's in a quantitative discipline (Computer Science, Statistics, Mathematics).

- 10 years in data science / applied machine learning, including 3+ years leading a data science / analytics team of 5 or more, with hiring and performance accountability.

Purpose of the job :

The purpose of the job is to partner with business and workstream teams to understand use cases, translate them into technical requirements, and lead the design and delivery of scalable, production-ready AI/ML solutions.

The role is responsible for building production-ready models from business use cases, using the most appropriate techniques across predictive, prescriptive, diagnostic, time-series, big data, computer vision, and Generative AI.

In addition, it builds, mentors, and scales a multidisciplinary team of data scientists, ML engineers, ML Ops specialists, and AI engineers. The role is accountable for end-to-end model delivery, governance, and measurable business impact within the agreed scope.

Responsibilities :

- Frame the analytical problem within each scoped use case; advise the Hub Lead on feasibility, effort and sequencing, and own the delivery plan across the team.

- Set the technical standard for problem framing, feature engineering, model development, validation, and documentation, applying established data science methodologies such as CRISP-DM and TDSP to ensure high-quality outputs from models with minimal iteration; personally lead the hardest or most ambiguous builds.

- Apply the most appropriate methods for each problem, including predictive, prescriptive, optimization, and time-series techniques. Where relevant, use Generative AI and LLMs, and assess emerging approaches such as Edge AI, deep neural networks, and digital twins.

- Partner with existing IT teams to deploy, operationalize, and scale business-agreed models into production.

- Own and accountable for model governance, validation, and technical risk management, while partnering with the business team on model usage and adoption.

- Own post-deployment monitoring, including model performance, drift detection, and retraining.

- Define data requirements and SLAs to the Data Engineering pillar; oversee CI/CD, monitoring and retraining delivered by the ML Ops engineers, sustaining model performance in production.

- Choose the most appropriate and cost-effective tools and techniques for each problem, and look for ways to simplify delivery through reusable and scalable solutions.

- Build, lead, mentor, and retain the Advanced Analytics team, while staying updated on emerging technologies to recommend innovative solutions.

- Take ownership of models and solutions delivered by external consultants, transfer their methodology and implementation approach into the Hub, and build internal capability so Apollo can own, maintain, and evolve the solutions over time.

Key Skills & Competencies :

- Hands-on Proficiency in complex exploratory data analysis, Predictive modeling, Computer Vision, Neural Network algorithms, Edge AI, Generative AI and AI on Digital Twin, Big data Analytics.

- Knowledge of AWS Cloud, and cloud-based data warehouses/ DBs (Redshift, Aurora, Timestream), Sagemaker ML, AWS Bedrock services, Keras, TensorFlow, PyTorch & Databricks for machine learning and data processing.

- Familiarity with MLOps practices, and CI/CD methodologies to streamline and automate the machine learning lifecycle.

- Knowledge of Ontology, Graph databases and AI TRiSM will be an advantage.

- Have solved complex business problems driving production deployment of AI/ML models in production and scaled up delivering significant savings / business value.

- Highly skilled in developing & Optimizing ML models that improves with new data and understand how to incorporate these ML models in core enterprise systems such as manufacturing systems.

- Delve into the data coming from different systems and different formats structured and unstructured, both in batch and streaming mode, to discover hidden relationships and useful information. Translate complex datasets into key insights and powerful business capabilities.

- Communication skills and good at presenting and explaining complex solutions to Business stakeholders.