#Hiring- Senior Data Scientist

Job Description & Requirements 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.