Senior Data Scientist

Senior Data Scientist

What You Will Do: Key Responsibilities

• Partner with business stakeholders

  to understand their needs, translate them into technical requirements, and develop data-driven solutions.

• Communicate complex AI concepts

  to non-AI stakeholders (Data Domains, Enablers, etc.) and facilitate multi-functional collaboration.

• Establish and enforce data governance policies and procedures

  to ensure the ethical and responsible use of data in AI applications.

• Implement Objective Key Results

  to measure the performance and impact of AI and automation solutions and regularly report on the progress and success of AI initiatives to the Head of Data & AI.

• Lead and manage complex data science projects

  from inception to deployment, ensuring successful delivery and impact on business objectives.

• Develop, implement, and optimize machine learning models and algorithms

  to solve real-world business problems.

• Perform data cleaning, wrangling, and transformation

  to ensure data quality and prepare it for analysis.

• Conduct exploratory data analysis (EDA)

  to uncover hidden patterns, trends, and insights within data sets.

• Develop clear and concise data visualizations

  to effectively communicate complex findings to both technical and non-technical audiences.

• Stay up to date on the latest advancements

  in data science methodologies and tools, and actively seek opportunities to incorporate them into our practices.

• Mentor and guide junior data scientists

  within the team, fostering a collaborative and learning environment.

The Skills You Bring: Required Qualifications

• Education:

  PhD in Statistics, Computer Science, Mathematics, or a related field; alternatively, a Master's or Bachelor's degree with significant work experience in the field.

• Experience:

  Proven track record of successfully leading and delivering data science projects with tangible business impact.

• AI/ML Expertise:

  Strong expertise in

  LLM (Large Language Models), deep learning, machine learning algorithms

  (e.g., supervised learning, unsupervised learning, reinforcement learning), and statistical modeling.

• Programming & Big Data:

  Proficiency in programming languages such as

  Python, R

  , and familiarity with big data processing tools (e.g., Spark, Hadoop) is a plus.

• Cloud AI Solutions:

  Familiarity in developing and deploying End-to-End (E2E) AI solutions in cloud platforms (Azure, AWS, GCP).

• Communication & Collaboration:

  Excellent communication and collaboration skills, with the ability to effectively translate complex technical concepts to a non-technical audience.

• Problem-Solving:

  Strong problem-solving skills and a passion for applying data science techniques to solve real-world problems.

• Work Management:

  Ability to work independently and manage multiple projects simultaneously while meeting deadlines.