Role Overview :

The Machine Learning Data Scientist is responsible for designing, building, and deploying data-driven and machine learning solutions that solve real business problems, while leading a team of ML practitioners and acting as the primary technical point of contact with client stakeholders. This role owns the full data science lifecycle from problem framing and data exploration to model development, deployment, and monitoring while also managing a team of resources and translating business use cases such as Anomaly Detection into actionable, production-grade ML solutions.

Key Responsibilities :

a. Data Exploration & Engineering :

- Collect, clean, preprocess, and transform structured/unstructured datasets.

- Perform exploratory data analysis (EDA) to identify patterns, trends, and anomalies.

- Engineer features for supervised, unsupervised, and time-series models.

- Handle noisy enterprise datasets (logs, transactional data, sensor/IoT data, CRM data).

- Implement data validation, quality checks, and augmentation pipelines.

b. Model Development & Evaluation :

- Design and build statistical and machine learning models for classification, regression, clustering, and anomaly detection.

- Apply techniques such as Isolation Forest, Autoencoders, One-Class SVM, and time-series anomaly detection for use cases like fraud, network, and operational anomaly detection.

- Perform hyperparameter tuning, cross-validation, and model evaluation using appropriate metrics (Precision, Recall, F1, AUC-ROC, etc.).

- Handle model drift, retraining strategies, and continuous model improvement.

- Work on RAG pipelines and vector search tuning where applicable for GenAI-augmented analytics.

c. Machine Learning & Deep Learning Engineering :

- Build ML/DL pipelines using Python, PyTorch, Scikit-learn, and/or TensorFlow.

- Implement NLP and time-series pipelines including tokenization, embeddings, and forecasting where relevant.

- Optimize models for latency, memory footprint, and cost efficiency in production.

- Integrate models into agentic AI and automation frameworks (e.g., LangChain, CrewAI) where applicable.

d. Team & People Management :

- Lead, mentor, and manage a team of ML/Data Science resources, including task allocation and performance reviews.

- Drive best practices in coding, experimentation, model documentation, and reproducibility across the team.

- Plan sprints/timelines for the team and ensure on-time delivery of models and analytics deliverables.

- Support skill development of team members on ML, DL, and MLOps practices.

e. Client Engagement & Use Case Management :

- Act as the primary point of contact with the client for defining, scoping, and prioritizing ML use cases (e.g., Anomaly Detection, Forecasting, Churn Prediction).

- Translate business requirements into data science problem statements and success metrics.

- Present model results, insights, and recommendations to client stakeholders in a clear, business-friendly manner.

- Gather client feedback and iterate on models/use cases to align with evolving business needs.

f. Monitoring & Production Support :

- Monitor model performance in production, focusing on accuracy, latency, and drift detection.

- Set up alerting and dashboards for anomaly detection use cases and other deployed models.

- Collaborate with engineering teams on CI/CD and MLOps practices for model deployment.

g. Security & Governance :

- Ensure secure handling of training data, including masking/anonymizing PII and sensitive enterprise data.

- Ensure compliance with enterprise and regulatory requirements (data governance, audit logging).

- Implement safeguards for model integrity, data leakage prevention, and responsible AI practices.

Required Expertise :

a. Core Data Science & ML :

- Strong foundation in statistics, machine learning algorithms, and deep learning.

- Hands-on experience with anomaly detection techniques and time-series analysis.

- Understanding of model evaluation metrics (Precision, Recall, F1, AUC-ROC, RMSE, etc.).

b. Tools & Technologies :

- Proficiency in Python, SQL, and ML frameworks such as Scikit-learn, PyTorch, and/or TensorFlow.

- Experience with data visualization tools (e.g., Power BI, Tableau, Matplotlib/Seaborn).

- Familiarity with cloud platforms (AWS/Azure/GCP) and MLOps tooling.

c. Leadership & Communication :

- Proven experience managing or mentoring a team of data scientists/ML engineers.

- Strong client-facing communication skills, with the ability to explain technical concepts to non-technical stakeholders.

- Experience gathering requirements and scoping use cases directly with clients.

d. Architecture Awareness :

- Understanding of microservices architecture and API-based model serving.

- Awareness of cloud-native and on-prem deployment models.