Machine Learning Lead

What is the role about?

We are looking for an experienced Machine Learning Lead to drive end-to-end AI/ML initiatives, from problem definition to production deployment. This role combines technical leadership, architecture design, and hands-on model development, ensuring scalable and business-aligned ML solutions. You will lead a team of data scientists and engineers while collaborating with cross-functional teams to build impactful AI-driven products.

Key Responsibilities :

Technical Leadership :

- Lead the design and development of scalable machine learning solutions

- Define ML architecture, model selection, and feature engineering strategies

- Guide teams on best practices in model development, evaluation, and deployment

- Review code, models, and pipelines to ensure high-quality delivery

Model Development & Optimization :

- Build and deploy models for :

1. Customer churn prediction

2. Recommendation systems

3. Demand forecasting

4. Customer segmentation

- Optimize models for :

1. Accuracy

2. Performance

3. Scalability

Data & Feature Engineering :

- Design robust data pipelines for :

1. Data ingestion

2. Feature engineering

3. Data validation

- Handle large-scale structured and unstructured datasets

MLOps & Deployment :

- Lead production deployment using :

1. AWS SageMaker / Lambda / APIs

2. Docker, CI/CD pipelines

- Implement :

1. Model monitoring

2. Retraining pipelines

3. Versioning and experiment tracking (MLflow)

Business Collaboration :

- Translate business problems into ML solutions

- Work with stakeholders to define KPIs and success metrics

- Drive data-driven decision-making across teams

Team Management :

- Mentor junior and mid-level data scientists

- Conduct technical reviews and knowledge-sharing sessions

- Build a high-performance ML team

Must-Have Criteria :

- 6+ years of experience in AI/ML or Data Science

- Strong expertise in :

1. Machine Learning (supervised & unsupervised)

2. Feature engineering & model tuning/optimization

- Proficiency in :

1. Python (NumPy, Pandas, Scikit-learn)

2. At least one : TensorFlow / PyTorch

- Strong understanding of :

1. Probability & Statistics

2. Linear Algebra

3. Data Mining Concepts and Problem understanding and Solving Skills

- Experience with :

1. Large-scale data processing

2. SQL and databases (PostgreSQL, MySQL, MongoDB)

- Hands-on experience in deploying ML models to production

- Strong debugging and optimization skills

Preferred Skills :

Experience with a wide range of machine learning use cases, including but not limited to :

- Recommendation systems

- Time series forecasting

- Customer analytics (churn prediction, segmentation, CLV)

- Classification and regression problems

- Anomaly detection and fraud detection

- NLP use cases (text classification, information extraction)

- Demand forecasting and inventory optimization

- Personalization and targeting models

- Ability to quickly understand and adapt ML solutions to new business problems across domains

- MLOps tools :

1. MLflow, Docker, Kubernetes

- Cloud platforms :

1. AWS (SageMaker, S3, Lambda)

- Experience in building :

1. REST APIs for ML inference

- Knowledge of :

1. Data visualization (Power BI, matplotlib, seaborn)

Leadership & Soft Skills :

- Strong problem-solving mindset

- Ability to lead and mentor teams

- Excellent communication with both technical and business stakeholders

- Experience handling end-to-end project ownership

Preferred Qualifications :

- Bachelors/Masters degree in :

1. Computer Science

2. Data Science

3. Mathematics

4. Statistics

5. Engineering

- Relevant certifications in AI/ML or Cloud (AWS/Azure/GCP)

Good to Have :

- Experience in GenAI / LLM-based systems

- Knowledge of :

1. RAG pipelines

2. Prompt engineering

- Experience in building and deploying ML solutions across multiple industry domains, including but not limited to :

1. Retail (recommendation systems, demand forecasting, customer segmentation)

2. Healthcare (clinical data analysis, predictive diagnostics, operational optimization)

3. Financial Services (fraud detection, risk modeling, credit scoring)

4. E-commerce and Digital Platforms

5. Logistics and Supply Chain

6. Telecom and Customer Engagement platforms

- Ability to understand domain-specific challenges and translate them into scalable, data-driven ML solution

(ref:hirist.tech)