Park Street H.o Kolkata West Bengal Full stack Jobs – 152 Open Jobs Today

Data Scientist
CodeClouds —Kolkata, West Bengal
- Full-time
- Azure
- Doctoral degree
- Research
- Health insurance
- Paid time off
AI/ML Engineer
Vixplor Analytics —Kolkata, West Bengal
- Analysis skills
- Machine learning
- AI
Shopify Developer
innofied —Kolkata, West Bengal
- Full-time
- Salesforce
- AWS
- JavaScript
- Health insurance
Content Writers
Excellis IT —Kolkata, West Bengal
- Full-time
- English
- Bachelor's degree
TechOps-Full Stack-Senior Manager- Kochi/TVM/Chennai
EY —Kolkata, West Bengal
TechOps-DE-AMS-Full Stack-Manager
EY —Kolkata, West Bengal
Remote UI Lead
Visteon Corporation —Kolkata, West Bengal
- Full-time
- Remote
- Java
- C++
Content writer
Vixplor Analytics —Kolkata, West Bengal
- B.Sc
- English
- Research
Operation Associate
ZORRRO DELIVERY —Kolkata, West Bengal
- ₹12,000 - ₹15,000 a month
- Full-time
- Microsoft Word
- Business development
- Bachelor's degree
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Sales Executive
Vixplor Analytics —Kolkata, West Bengal
- English
- Communication skills

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AI/ML Engineer
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Looking for an AI/ML expert with 4+ years of experience in designing, developing, and deploying machine learning models, strong analytical and programming…

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Data Scientist

CodeClouds -
Kolkata, West Bengal

Apply Now

Job details

Full-time

Benefits

Health insurance
Paid time off

Qualifications

TensorFlow
CI/CD
Statistics
Power BI
Azure
Statistics
Doctoral degree
Cassandra
Kubernetes
Calculus
Big data
Enterprise Software
Spark
NumPy
R
NoSQL
Maths
Git
Research
MongoDB
Tableau
Survival analysis
Master's degree
SQL
Pandas
AWS
Julia
Docker
Sentiment analytics
Bachelor's degree
Machine learning
Scala
Continuous integration
Deep learning
Natural language processing
S3
Kafka
Data science
Keras
Redshift
AI
Graph databases
Python
Analytics
Hadoop

Full job description

Job Details

Available Locations

Kolkata, India

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

Apply Now

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Roles & Responsibilities

Advanced Analytics & Modeling

Machine Learning & AI

Enterprise-Scale Data Engineering

MLOps & Production Deployment

Strategy, Leadership & Collaboration

Technical Skills & Tools

Mathematics & Statistics:

Machine Learning & AI:

Programming & Libraries:

Cloud, MLOps & Infrastructure:

Enterprise & Governance:

Soft Skills & Leadership

Qualifications

Why Join

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