Qualifications
The ideal candidate should possess:
Must-have
- Strong ability to communicate complex quantitative analysis in a concise, actionable manner.
- Proven experience working with high-volume, high-dimensional structured and unstructured data.
- Strong expertise in feature selection and feature engineering across diverse data types.
- Solid grounding in machine learning techniques (supervised and unsupervised).
- Deep understanding of advanced analytics (statistics, NLP, optimization, simulation).
- Strong programming skills in Python and/or R experience with Apache Spark or similar frameworks.
- Experience using LLMs for GenAI or Agentic AI solution development.
- Hands-on experience with data visualization tools and libraries (e.g. Tableau, Qlik, Plotly, ggplot2, Shiny).
- Experience in model deployment and lifecycle management using Docker and Kubernetes.
Nice to have
- Postgraduate degree (Master's or PhD) in Mathematics, Statistics, Business Analytics, or a related field.
- Prior consulting experience in AI and data analytics domains.
- Experience delivering advanced analytics solutions or conducting applied research.
- Exposure to cloud and big data platforms (AWS, Azure, Hadoop, Spark, Cloudera).
- Experience with DevOps practices in analytics delivery.
- Background in application or software development.
- Exposure to deep learning, reinforcement learning, or graph analytics.
- Knowledge of database modelling and data warehousing concepts.
Job Description
As a Data Scientist, you will design, develop, and deploy advanced analytics and machine learning solutions that uncover hidden insights from large, complex datasets. You will work closely with business stakeholders, project managers, and engineering teams to translate real-world business challenges into production-ready data science solutions. This role combines hands-on analytics development, applied research, and client advisory responsibilities, supporting organizations on their data science and AI journey.
- Translate customer pain points into clear analytical problem statements and solution architectures.
- Design, build, and iterate end-to-end data science workflows, from data ingestion and preprocessing to feature engineering, modelling, and deployment.
- Apply statistical analysis, machine learning, NLP optimizations, and simulation techniques to solve complex business problems.
- Perform statistically sound model validation and clearly justify model selection and performance
Model Engineering & Production Deployment
- Build scalable, efficient machine learning models for deployment in production systems.
- Operationalize analytics workflows using Python/R and distributed processing frameworks such as Apache Spark.
- Deploy and manage models using containerization and orchestration tools (e.g. Docker, Kubernetes).
- Leverage LLMs to build GenAI or Agentic AI solutions where appropriate.
Insights Communication & Visualization
- Design and develop impactful dashboards and visualizations to communicate actionable insights.
- Present results, learnings, and recommendations clearly to both technical and non-technical audiences.
- Act as a trusted adviser to clients in conceptualizing and evaluating advanced analytics solutions.
Collaboration & Delivery
- Work closely with project managers and technical leads to provide regular status updates and refine analytics requirements.
- Contribute to data architecture and engineering decisions that support analytics use cases.
- Participate in interdisciplinary teams delivering projects using Agile or Waterfall methodologies.
- Knowledge Sharing & Mentorship
Contribute to internal communities of practice and special interest groups.
- Mentor and upskill junior data scientists and peers, depending on seniority.
