Sagility - Manager/Senior Manager - Data Science (8-12 yrs)

Job Title : Data Science Manager / Senior Manager - Technical Leadership in ML

Location : [Insert Location]

Department : Data Science & AI

Reporting To : VP / Director of Data Science

Role Summary :

We are looking for a technically strong and visionary Data Science Manager / Senior Manager to lead a team of data scientists and ML engineers in building scalable, production-grade AI solutions. This role demands deep expertise in machine learning, hands-on technical leadership, and the ability to architect and guide complex data science initiatives from ideation to deployment.

Experience Requirements :

- Should have worked on minimum 6 academic projects and 2 Real world AI-ML Projects which are deployed in production.

Sagility Values :

- Sagility believes in Doing Right by its People.

Qualification and Skills :

- 1. Preference : Math, Stats Science/Arts Graduates with Any programming language (R/Py/Ruby/C+) knowledge and practical implementation of Statistical Modelling and Machine Learning, conceptual clarity of Math and Stats application in real world.

- 2. Modelling : Supervised & Unsupervised, Classification and Regression, Boosting, Data Mining (core exploratory data mining) tasks, Survival Modelling, Loss Forecasting (Insurance).

- 3. Machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests, etc.

- 4. Deep Learning Models.

- 5. Programming Skills : Python, hands on experience in handling big data, writing programs, classes, functions.

- 6. Statistics : Good applied statistical skills, including measure of dispersion, variation, sampling, correlation and causation, knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for this role.

- 7. Needs to have academic or practical experience of formulating a DS project.

- 8. Experience with Data Visualization Tools like matplotlib, ggplot, d3.js, Tableau that help to visually encode data.

Key Responsibilities :

1. Technical Leadership :

- Architect and oversee the development of advanced ML models including supervised, unsupervised, and deep learning approaches.

- Drive best practices in model development, validation, deployment, and monitoring.

- Lead code reviews, model audits, and ensure reproducibility and scalability of solutions.

- Champion MLOps practices and CI/CD pipelines for ML workflows.

2. Team Management :

- Mentor and grow a team of data scientists and ML engineers.

- Foster a culture of technical excellence, ownership, and continuous learning.

- Set clear goals, provide feedback, and support career development.

3. Strategic Execution :

- Translate business problems into data science solutions with measurable impact.

- Prioritize projects based on ROI, feasibility, and strategic alignment.

- Collaborate with cross-functional teams including engineering, product, and domain experts.

4. Innovation & Thought Leadership :

- Stay ahead of the curve on emerging ML techniques, tools, and frameworks.

- Evaluate and integrate cutting-edge technologies into the teams workflow.

- Represent the data science function in technical forums and leadership discussions.

Required Qualifications :

- Expertise in Computer Science, Machine Learning, Statistics, or related field (Min Graduate).

- 8+ years of experience in data science, with 3+ years in a technical leadership role.

- Proven track record of deploying ML models in production environments.

- Strong programming skills in Python (preferred), R, or Scala.

- Deep understanding of ML frameworks (e.g., PyTorch, TensorFlow, XGBoost, scikit-learn).

- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).

- Familiarity with MLOps tools (MLflow, Airflow, SageMaker, etc).