Key accountabilities

- A key part of the role will be the acquisition and development of commercial skills and knowledge, learning from others in the direct team and the wider business.
- Conceive, develop, deploy and monitor end-to-end, data-driven solutions to support Company initiatives, working individually or as part of a small cross-functional team.
- Develop and work on the integration of end-to-end software solutions, including, machine learning and artificial intelligence algorithms, or custom algorithm development.
- Identify opportunities for innovation through analytics in the Organisation. Evaluate potential, scope projects, and define deliverables.
- Strongly contribute in agile cross functional teams, bringing the development skills for robust integration of AI products into technical systems.
- Measure and track impact of the products developed through business KPIs. Present result of analyses to business owners and share knowledge with team members.
- Support the simplification of processes so that business owners, in addition to data scientists, can leverage our data to drive business outcomes.
- Investigate new data and analytics technologies and contribute to the continual development and evolution of our data architecture.

Technical skills The core areas of skill in Data Science are Mathematics, Programming and Domain Expertise. Individuals have varying levels of skill across these categories, and very few people are highly skilled in all, so these items are used as a guide for relevant skills, but not a definitive tick list of mandatory items. Curiosity and continuous learning are key traits of successful data scientists.

A Data Scientist should have detailed knowledge in at least one of Maths or Programming domains,



and will develop knowledge and experience in the other, as well as the insurance domain.

Mathematics and statistics

- Linear Algebra and Calculus - Probability and statistics
- Machine Learning theory and algorithms. e.g regression, association rule mining, clustering analysis, pattern recognition

Programming

- Advanced proficiency in Python and its associated data science libraries with experience writing clean, well-documented, and unit-tested code.
- Solid foundation with a variety of machine learning models, including but not limited to gradient boosted models, generalized linear models and large language models.
- Extensive experience with text based data, including Natural Language Processing libraries such as BERT and NLTK
- Hands-on experience with Natural Language Processing (transformers/LLMs, embeddings, prompt engineering, RAG) and classical machine learning.
- Visualisation frameworks. e.g. Matplotlib, Seaborn, Plotly, Bokeh, D3
- Hands-on knowledge deploying production-ready solutions ensuring robustness, scalability, and alignment with business goals.
- Demonstrated experience reviewing the work of colleagues and version control using Git.
- Able to deliver projects and articulate technical concepts to non-technical audiences.
- Innovative and willing to seek creative solutions that improve on conventional approach.
- Pipeline and deployment tools. e.g. MLFlow, Kubeflow, Flask,



FastAPI
- Data processing and manipulation. e.g. Pandas, Polars, Spark
- Data Stores. Relational (e.g. Postgres, MySQL, Oracle), NoSQL (e.g. MongoDB, ElasticSearch, Neo4J, Redis)
- Cloud computing platforms and tools. e.g. Azure, AWS, docker, kubernetes
- DevOps methods and tools. e.g. git, CI/CD, Jira, Confluence, Gitlab/Github

Qualifications

- Whilst not strictly mandatory, Data Scientists are normally educated to Advanced Degree (i.e. Masters) or PhD level, in a relevant discipline (including numerate, engineering, computer science specialisms), due to the highly specialized and detailed nature of the skills used in the role.
- For individuals who do not have the necessary academic qualifications, enrolment on a Company supported Data Science Degree/Master (such as Avado L7) may be an acceptable alternative

Experience Experience of components in the data science lifecycle, gained through work exposure, or through studies, including:

- Collecting and Integrating Data from a variety of sources, and in different forms (structured, unstructured, database, API, web etc)
- Exploring and Understanding Data using a variety of tools and methods and communicate findings to business stakeholders, with support and guidance from experienced peers
- Preparing and Refining Data through feature selection, processing, manipulation and transformation techniques in order to ready it for machine learning algorithms
- Build and Evaluation of Machine Learning Models using various supervised, unsupervised and reinforcement learning algorithms
- Monitoring Live Performance including system led KPIs such as latency and business led KPIs such as model accuracy and benefit

📌 Senior Data Scientist (Maharashtra)
🏢 Allianz Services
📍 Maharashtra