Lead Data Scientist

Apply who can available to take interview this week

Role-Data Scientist

Mandatory skill- anomaly

Exp-8 to 10yrs

Location-Only Pune

Budget- 25lpa to 27lpa



 Experience: Candidate must have hands-on experience in the field of Anomaly/Fraud/Outlier Detection and the connected technologies. 

 Advanced Model Development and Implementation: 

 Design and develop sophisticated machine learning models specifically tailored for fraud and anomaly detection using state-of-the-art techniques. 

 Implement Graph Neural Networks (GNNs) using PyTorch Geometric to capture complex relationships and dependencies in graph-structured data. 

Develop algorithms for Graph Anomaly Detection, focusing on node, edge, and subgraph anomaly identification. 

Have a know-how of various Graph Neural Network algorithms like GCN, GraphSAGE, GAT, GINE, Graph Message Passing, Hetero Graph Learning etc. 

 Have very good understanding and know how of various NLP techniques and models like BERT, Text Classification, Text Extraction, LLMs and LLM Fine-Tuning etc. 

 Data Analysis and Feature Engineering: o Perform in-depth exploratory data analysis (EDA) to uncover hidden patterns and insights related to fraudulent activities. Engineer features from large-scale datasets, including constructing graph-based features such as node embeddings, edge attributes, and graph metrics. 

 Utilize techniques such as spectral clustering and community detection to enhance feature representation in graph data. 

 Utilize various nlp techniques for data cleaning, visualization, data pre-processing and data preparation o Research and Innovation: 

 Conduct cutting-edge research on emerging methodologies in fraud detection, anomaly detection, and graph-based machine learning.

 INTERNAL o Experiment with novel architectures and algorithms, such as attention mechanisms in GNNs, to improve detection capabilities. 

 Prototype and evaluate new approaches using rigorous experimental design and statistical validation. 

 Collaboration and Communication: 

 Work closely with other data scientists to ensure seamless integration of models into production environments, optimizing for scalability and performance. 

 Collaborate with software developers to implement efficient data pipelines and real-time processing systems.

Technical Skills: 

Programming and Libraries Proficiency in Python, with extensive experience in libraries such as PyTorch, PyTorch Geometric, TensorFlow, Keras, Transformers and Scikit-learn. 

 Candidate must know how to work with Graph Based Machine Learning and NLP. Experience with graph processing frameworks and lib