Data Scientist - GenAI focused

Position Overview

As a Data Scientist, you will play a critical role in advancing our analytical capabilities. You will

be instrumental in distilling vast amounts of data into actionable insights, thereby influencing

our strategic direction. The ideal candidate will possess a robust analytical skill set, data and AI

modelling skills, and the ability to convey complex data concepts to stakeholders with varying

degrees of technical knowledge.

Responsibilities

 End-to-End Design and Implementation: Play a key role in the end-to-end design and

implementation of AI platform, powering use cases across training and serving models

 Product Ownership and Implementation: Exercise strong end-to-end product

ownership by translating product requirements into user interfaces and backend

distributed system designs and overseeing their implementation

 Core Platform Infrastructure: Design and build the core platform infrastructure that

supports customer-facing product features

 Reliability, Security, and Scalability: Ensure the reliability, security, and scalability of

the backend distributed systems that power all aspects of clients’ solutions

 Supervision and Mentorship: Supervise and mentor junior team members, guiding

them in identifying key development areas and collaborating with non-technical team

members to develop a customized AI platform for clients

 Data Analysis: Employ analytical techniques to deconstruct and interpret complex data

sets. Deliver clear, actionable insights from voluminous and diverse data sources

 Predictive Modelling: Craft and refine predictive models and advanced algorithms to

anticipate market trends, customer behaviour, and other business-critical outcomes

 Experimental Design and Hypothesis Testing: Devise robust scientific methods to test

hypotheses, including controlled A/B testing and multivariate analysis to validate

models and approaches

 Strategic Data Visualization: Develop intuitive and compelling data visualizations and

interactive dashboards that resonate with both specialist and non-specialist audiences

 Feature Engineering: Identify and engineer sophisticated features to enhance model

performance and predictive reliability

 Model Building and Evaluation: Implement rigorous validation processes to gauge

model effectiveness, adjusting for factors like overfitting and underprediction to refine

overall performance

 Performance measurement: Develop metrics and KPIs to measure and monitor data

operations performance, ensuring efficiency and effectiveness

 Data security: Ensure data operations comply with relevant data protection regulations

(e.g., GDPR) and implement security measures to protect sensitive data

 Data Source Exploration: Proactively explore and assimilate new data sources, staying

ahead of market trends to fuel innovation and improvement

 Cross-functional Collaboration: Work collaboratively with data engineers, data

architects and with client stakeholders across the business to understand operational

challenges and deliver data-centric solutions that align with company objectives

 Comprehensive Documentation: Maintain meticulous documentation of best practices,

methodologies, data lineage, model development, and analytical findings to ensure

transparency and reproducibility

 Continuous Learning: Remain at the vanguard of data science by keeping abreast of

emerging technologies, methodologies, and industry best practices

Qualifications

 Bachelor’s or master’s degree in computer science, computer engineering, information

technology, statistics, mathematics, business analytics or a related quantitative field

 Demonstrable experience of minimum 7 years as a Data Scientist or in a similar data-

centric role, with a portfolio of projects that exhibit depth in data analysis, data

engineering, and AI modelling.

 Strong Experience in building and fine-tuning GEN AI solutions will be an added

advantage.

 Experience applying software engineering methodologies and best practices including

coding standards, code reviews, build processes, testing, and security.

 Prior experience in developing AI solutions on public cloud services is an advantage.

 Technical Expertise:

o Programming languages: Python (pandas, NumPy, scikit-learn, , R, Java, Scala

o Statistical analysis & machine learning: Regression, classification, clustering,

neural networks, time-series forecasting, deep learning etc.

o Generative AI: Experience in building and fine-tuning GEN AI solutions.

o Data management & databases: Oracle, SAP, SQL, NoSQL (e.g., MongoDB,

Cassandra), Data Warehousing

o Big data technologies: Apache Hadoop, Spark, Kafka, etc.

o Cloud platforms: Microsoft Fabric, Azur