Lead Data Scientist -AI

Key Responsibilities:

• Research & Innovation:

o   Stay up-to-date with the latest advancements in deep learning, LLMs, and statistical methods.

o   Conduct experiments to explore new techniques and improve existing models.

o   Perform applied research and experimentation, validate model performance for accuracy, robustness & champion responsible AI practices

• Deep Learning & LLM Development:

o   Design, implement, and optimize deep learning models and frameworks (e.g., TensorFlow, PyTorch, JAX).

o   Develop and fine-tune large language models (LLMs) for specific use cases, including natural language processing (NLP) tasks.

o   Experiment with state-of-the-art architectures (e.g., Transformers, GPT, BERT) to solve complex problems.

o   Knowledge of Agentic frameworks (Crew AI , llama index etc).

o   Good knowledge of GenAI framework

• Statistical Modelling & Analysis:

o   Apply advanced statistical methods to analyse data, validate models, and interpret results.

o   Develop probabilistic models and leverage Bayesian inference techniques where applicable.

o   Ensure models are statistically robust and generalize well to real-world data.

• Data Preprocessing & Feature Engineering:

o   Clean, preprocess, and transform large datasets for training and evaluation.

o   Perform feature engineering and dimensionality reduction to improve model performance.

• Model Deployment & Optimization:

o   Deploy deep learning models and LLMs into production environments.

o   Optimize models for inference speed, memory usage, and scalability.

o   Implement monitoring and evaluation systems to track model performance over time.

• Collaboration & Leadership:

o   Work closely with cross-functional teams, including data scientists, engineers, and product managers.

o   Mentor junior team members and provide technical guidance.

Requirements and skills

• 7+ years in applying AI/ML principles to real world applications

• Broad NLP knowledge: tokenisation, part-of-speech tagging, dependency parsing, syntactic parsing, word sense disambiguation, topic modeling; contextual text mining, Word embedding

• Experience Computer Vision:

o   Construction, Feature detection, Segmentation, Classification

o   object detection, tracking, localisation, classification, recognition, scene understanding

• Experience to Deep Learning - CNNs, LSTMs, network architecture, network tuning, transfer learning, multi-task learning

• Machine Learning experience - Algorithm Evaluation, Preparation, Analysis, Modeling and Execution.

• Experience to Open source NLP libraries e.g. NLTK, Regex, Stanford NLP, OpenNLP/CoreNLP

• Very strong grasp of IT concepts with a strong algorithms/data structures background

• Demonstrated history of building prototypes to win business confidence.

• Experience in using Keras, Tensorflow, Caffe and/or other neural network development frameworks.

• Experience with common data science toolkits, such as Scikit, NumPy, R libraries - Excellence in at least one of these is mandatory.

• Proficiency with any one NoSQL databases such as MongoDB, Cassandra, HBase.

• Should have prior experience in developing APIs, services using either C# or Java.

• Experience in Develop, implement, and optimize machine learning models using the Microsoft AI Platform (Azure Machine Learning, Cognitive Services, etc.)

• Excellent understanding of machine learning techniques and algorithms, such as SVM, Decision Forests, k-NN, Naive Bayes etc.

• Experience in selecting features, building and optimizing classifiers using machine learning techniques.

• Should have good awareness on entire machine learning/ predictive modeling & implementation.

Additional Qualifications:

• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras. 

• Knowledge of natural language processing (NLP) and computer vision techniques. 

• Familiarity with DevOps practices and CI/CD pipelines for machine learning models. 

• Microsoft Azure certifications (e.g., Azure Data Scientist Associate, Azure AI Engineer Associate). 

• Research business domain and develop use-cases to support enterprise wide AI solutions.

• Monitor and maintain deployed models, ensuring scalability, performance, and accuracy.

• Prior experience with data visualization tools, such as D3.js, GGplot, etc..

• Good knowledge on statistics skills, such as distributions, statistical testing, regression, etc..

• Adequate presentation and communication skills to explain results and methodologies to non-technical stakeholders.

• Basic understanding of the banking industry is value add.

We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status”.

At Société Générale, we are convinced that people are drivers of change, and that the world of tomorrow will be shaped by all their initiatives, from the smallest to the most ambitious. Whether you’re joining us for a period of months, years or your entire career, together we can have a positive impact on the future. Creating, daring, innovating, and taking action are part of our DNA. If you too want to be directly involved, grow in a stimulating and caring environment, feel useful on a daily basis and develop or strengthen your expertise, you will feel right at home with us!

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