Available Locations

Kolkata, India

Category

Software Developer Jobs

Employment Type

Full-Time

Seniority

Senior

Experience Required

7+ Years

Skills Required

Mathematics, statistics, machine learning, deep learning, reinforcement learning, and optimization, combined with enterprise-scale data engineering, cloud platforms, and MLOps expertise.

Job Description

We are seeking an exceptionally skilled Enterprise Data Scientist to lead cutting-edge analytics, AI, and machine learning initiatives across the organization. This role requires mastery of mathematics, statistics, machine learning, deep learning, reinforcement learning, and optimization, combined with enterprise-scale data engineering, cloud platforms, and MLOps expertise. You will design, deploy, and monitor high-impact AI/ML systems that drive enterprise strategy, operational efficiency, and business innovation.

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 Advantages

Retirement Benefits

Partial Course Funding

Team Building Activities

📌 Data Scientist (India)
🏢 CodeClouds
📍 India