Senior Data Scientist - Python (4-6 yrs)

Requirements :

- 4+ years in advanced analytics, statistical modelling, and machine learning.

- Best practice knowledge in credit risk -strong understanding of the full lifecycle from origination to debt collection.

- Well-versed with ML algos, BIG data concepts, and cloud implementations.

- High proficiency in Python and SQL/NoSQL.

- Collections and Digital Channels are a plus.

- Strong organizational skills and excellent follow-through.

- Outstanding written, verbal, and interpersonal communication skills.

- High emotional intelligence, a can-do mentality, and a creative approach to problem solving.

- Takes personal ownership, Self-starter - ability to drive projects with minimal guidance and focus on high-impact work.

- Learns continuously; Seeks out knowledge, ideas, and feedback.

- Look for opportunities to build one's skills, knowledge, and expertise.

- Experience with big data and cloud computing, viz. Spark, Hadoop (MapReduce, PIG, HIVE)

- Experience in risk and credit score domains preferred.

Job Responsibilities :

- Partner with stakeholders to identify business challenges and translate them into actionable data science projects with a focus on AI and Generative AI applications.

- Design and conduct advanced data analysis, leveraging AI techniques like deep learning and natural language processing, to extract meaningful insights from complex datasets.

- Develop, train, and evaluate cutting-edge machine learning models, including Generative Adversarial Networks (GANs) and transformers, for tasks like image/text generation, data augmentation, and creative content development.

- Champion responsible AI practices, ensuring models are fair, and unbiased, and mitigate potential risks.

- Collaborate with cross-functional teams (engineering, product, marketing) to integrate AI and Generative AI solutions seamlessly into our products and workflows.

- Lead and mentor junior data scientists, fostering a culture of continuous learning and innovation.

- Should have working experience on LLMs including prompt engineering, fine-tuning, and explainability

- Woking knowledge in advance Retrieval Augmented Generation (RAG) and ability to apply on structured and unstructured data

- Communicate complex technical concepts clearly and concisely to both technical and non-technical audiences.

- Formulates and leads guided, multifaceted analytic studies against large volumes of data.

- Interprets and analyses data using exploratory mathematical and statistical techniques based on the scientific method.

- Coordinates research and analytic activities utilizing various data points (unstructured and structured) and employs programming to clean, massage, and organize the data.

- Leads all data experiments tasked by the Data Science Team. Coordinates with Data Engineers to build data environments providing data identified by Data Analysts, Data Integrators, Knowledge Managers, and Intel Analysts.

- Working knowledge of MLOps and establishing the ML pipelines

- Works closely with all business units and engineering teams to develop strategy for long-term data platform architecture.

- Working experience on production-grade Data Science problems and handling large amount of data

- Evaluate ML and LLM performance using appropriate metrics and benchmarks.

- Ensure compliance with data privacy regulations and ethical AI practices, particularly in handling textual data.

- Implement security measures to protect sensitive data.

- Lead the data-driven decision-making process, from data collection and analysis to implementation and monitoring of solutions

- Collaborate with cross-functional teams to understand business challenges and objectives, translating complex data into actionable insights.