Senior Data Scientist
Key Responsibilities and Results
- Lead data science initiatives supporting offerings by partnering with Marketing, Product, and external stakeholders; define problem statements and architect end-to-end AI/ML solutions, including Generative AI and LLMs.
- Design, build, and productionize analytical models for intelligent decisioning within customer journeys; integrate models into decision engines while ensuring governance, scalability, and performance monitoring.
- Analyze customer behaviour across Mobile, Broadband, and entertainment services to generate actionable insights on lifecycle, segmentation, and targeting strategies.
- Drive data exploration, experimentation, and optimization through A/B testing, uplift modelling, and campaign analytics.
- Provide technical mentorship to junior data scientists and uphold high standards in code quality and best practices.
- Communicate complex analytical insights clearly to both technical and non-technical stakeholders, including senior leadership.
Major Challenges / Typical Problems Encountered
- Apply strategic and innovative thinking to continuously improve customer engagement and retention.
- Adapt quickly to a dynamic and competitive market environment with evolving business needs.
- Manage and influence multiple stakeholders while aligning cross-functional teams to achieve business outcomes.
Decision Making Authority
- Independently select technical approaches, methodologies, and prioritization of analytical use cases.
- Propose proof-of-concepts for emerging technologies such as Generative AI and LLMs.
- Escalate decisions involving major production changes, compliance, or security implications to higher authority.
Skills for Success:
Qualifications & Experience
- Bachelor’s or Postgraduate degree in Computer Science, Mathematics, Statistics, or related field, with at least 3 years of relevant experience.
- Experience in telecom or insurance analytics is advantageous.
Technical / Professional Skills
- Strong expertise in machine learning and statistical modelling (e.g., regression, time series, clustering, causal inference, neural networks).
- Experience with Generative AI and LLMs, including fine-tuning, prompt engineering, and evaluation.
- Proficiency in data tools and platforms such as SQL, Python, Spark, Hadoop/Hive, Databricks, and Power BI.
- Familiarity with software engineering best practices and version control (GitHub, GitLab, Bitbucket).
- Exposure to cloud platforms (AWS, Azure, GCP) and ML lifecycle tools (MLflow, Airflow) is a plus.
Non-Technical / Soft Skills
- Strong analytical and problem-solving capabilities with a business-first mindset.
- Collaborative team player with a strong customer focus.
- Excellent communication and data storytelling skills.
- Ability to mentor and guide junior team members.