Machine Learning Manager (7-12 yrs)

We are seeking an experienced Machine Learning Manager with hands-on experience in designing, developing, and deploying machine learning models and systems.

The ideal candidate will work closely with data scientists, software engineers, and product teams to create solutions that drive business value. You will be responsible for building scalable and efficient machine learning pipelines, optimizing model performance, and integrating models into production

Key Responsibilities :

- Develop and train machine learning models, including supervised, unsupervised, and deep learning algorithms, to solve business problems.

- Collaborate with data engineers to clean, preprocess, and transform raw data into usable formats for model training and evaluation.

- Deploy machine learning models into production environments, ensuring seamless integration with existing systems and monitoring model performance.

- Create and test new features to improve model performance, and optimize feature selection to reduce model complexity.

- Research and implement state-of-the-art algorithms to improve model accuracy, efficiency, and scalability.

- Work closely with data scientists, engineers, and other stakeholders to understand business requirements, develop ML models, and integrate them into products and services.

- Model Evaluation : Conduct model evaluation using statistical tests, cross-validation, and A/B testing to ensure reliability and generalizability.

- Keep abreast of new techniques and advancements in the field of machine learning, and suggest improvements for internal processes an models.

Required Skills & Qualifications :

- Education : Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.

- Experience : Hands-on experience working as a machine learning engineer or in a related role.

Programming Languages :

- Proficiency in Python (preferred), R, or Java. Experience with ML libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras.

- Strong knowledge of SQL and experience working with large datasets (i.e. using tools like Pandas, NumPy, Spark).

- Experience with cloud platforms like AWS, Google Cloud, or Azure, particularly with ML services such as SageMaker or AI Platform.

- Model Deployment : Hands-on experience with deploying ML models using Docker, Kubernetes, and CI/CD pipelines.

- Mathematics and Statistics : A solid foundation in mathematical concepts related to ML, such as linear algebra, probability, statistics, and optimization techniques.

Preferred Qualifications :

- Experience with deep learning frameworks (i.e., TensorFlow, PyTorch).

- Exposure to natural language processing (NLP), computer vision, or recommendation systems.

- Familiarity with version control systems (i.e., Git) and collaborative workflows.

- Experience with model interpretability and fairness techniques.

- Familiarity with big data tools (i.e., Hadoop, Spark, Kafka)