Data Scientist – AI, Machine Learning & Analytics

Roles & Responsibilities

1.

Advanced Analytics & Modeling

• Design, implement, and deploy predictive, prescriptive, and optimization models at enterprise scale.
• Apply mathematical modeling including linear algebra, multivariate calculus, stochastic processes, graph theory, and optimization techniques (convex, non-convex, combinatorial).
• Develop statistical models: Bayesian inference, hierarchical modeling, time series, survival analysis, causal inference, and probabilistic graphical models.
• Lead feature engineering, dimensionality reduction, embeddings, and representation learning for complex datasets.
2.

Machine Learning & AI

• Implement supervised, unsupervised, semi-supervised, and reinforcement learning algorithms.
• Build deep learning architectures: CNNs, RNNs, LSTMs, Transformers, Graph Neural Networks (GNNs), and Autoencoders.
• Develop NLP solutions: embeddings, word2vec, BERT, GPT-style models, sequence modeling, summarization, and sentiment analysis.
• Apply graph analytics for network data, knowledge graphs, and recommendation systems.
• Evaluate models rigorously using cross-validation, AUC, precision/recall, F1, SHAP/LIME, and advanced statistical tests.
3.

Enterprise-Scale Data Engineering

• Architect and maintain large-scale data pipelines using Spark, Hadoop, Kafka, Flink, or equivalent.
• Integrate heterogeneous structured, semi-structured, and unstructured data from multiple enterprise systems.
• Ensure data quality, governance, security, and compliance (GDPR, HIPAA, SOC2).
4.

MLOps & Production Deployment

• Implement MLOps best practices: model versioning, automated testing, CI/CD pipelines, containerization, and orchestration.
• Deploy models on cloud platforms (AWS SageMaker/EMR, GCP Vertex AI, Azure ML) and monitor performance and drift.
• Build real-time and batch inference pipelines for high-throughput enterprise applications.
5.

Strategy, Leadership & Collaboration

• Translate complex analytical outputs into actionable business insights for executives.
• Collaborate with engineering, product, finance, operations, and marketing teams to embed AI/ML solutions.
• Mentor and guide junior and mid-level data scientists, fostering best practices in modeling, coding, and documentation.
• Lead R&D initiatives, exploring emerging AI/ML technologies and innovation opportunities.

Technical Skills & Tools

Mathematics & Statistics:

• Linear algebra, calculus, probability, stochastic processes, discrete math, graph theory, combinatorics.
• Regression, Bayesian statistics, hypothesis testing, multivariate analysis, time series forecasting, survival analysis, causal inference, and optimization.

Machine Learning & AI:

• Supervised, unsupervised, semi-supervised, reinforcement learning.
• Ensemble methods, deep learning (CNN, RNN, LSTM, Transformers, GNN), NLP, autoencoders, embeddings.
• Model interpretability: SHAP, LIME, Integrated Gradients.
• Hyperparameter optimization, grid search, Bayesian optimization.

Programming & Libraries:

• Python (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM), R, Scala, Julia.
• Big Data: Spark MLlib, Hadoop, Kafka, Flink, Dask.
• SQL, NoSQL (MongoDB, Cassandra), Data Warehouses (Redshift, Snowflake, BigQuery).
• Visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI.

Cloud, MLOps & Infrastructure:

• AWS (S3, SageMaker, EMR, Lambda), Azure (ML Studio, Databricks), GCP (BigQuery, Vertex AI).
• Containers & Orchestration: Docker, Kubernetes.
• MLOps frameworks: MLflow, Kubeflow, TFX, Airflow, Prefect.
• Monitoring & Logging: Prometheus, Grafana, ELK stack, DataDog.

Enterprise & Governance:

• Data governance, security, compliance (GDPR, HIPAA, SOC2).
• Scalable architecture for multi-terabyte datasets.
• Knowledge of CI/CD pipelines, version control (Git), reproducible research practices.

Soft Skills & Leadership

• Ability to communicate complex models in simple business terms.
• Strong strategic thinking, problem-solving, and critical reasoning.
• Mentoring and developing high-performing data science teams.
• Leading cross-functional projects and influencing enterprise AI strategy.

Qualifications

• PhD, Master's, or Bachelor's in Mathematics, Statistics, Computer Science, Data Science, Operations Research, or related field.
• 7–15+ years of enterprise-scale data science, ML, and AI experience.
• Proven experience deploying production ML systems at scale.
• Hands-on expertise in mathematical modeling, optimization, AI/ML, deep learning, and cloud platforms.
• Experience leading analytics teams and enterprise-wide AI/ML projects.

Why Join

• Lead enterprise AI/ML innovation at scale.
• Collaborate with top-tier technical and business leaders.
• Access to state-of-the-art tools, cloud infrastructure, and cutting-edge research.
• Drive impactful decisions and strategic initiatives across the organization.

What we offer you

• Flexible Working

• Competitive Compensation

• Insurance Benefits

• Training & Mentoring

• Frequent Celebrations

• Home Office Allowance

• Paid Leave Benefits

• Retirement Benefits

• Partial Course Funding

• Team Building Activities