Data Scientist

The Data Scientist will be responsible for leveraging advanced analytics, statistical modeling, and AI/ML techniques to deliver actionable insights and innovative data-driven solutions. The role requires a blend of technical expertise, analytical thinking, and stakeholder management skills to support strategic decision-making and optimize business performance.

Primary Skills

• Problem Solving - Defines complex problems, generates data-driven solutions, and evaluates alternatives to determine optimal outcomes.

• AI Ethics - Applies ethical principles and best practices to ensure the responsible use of artificial intelligence technologies.

• Data Analysis - Collects, interprets, and analyzes large datasets to uncover trends and insights.

• Data Wrangling - Cleans, manipulates, and transforms data into formats suitable for analysis and modeling.

• Statistics - Applies appropriate statistical methodologies to support analysis and decision-making.

• Statistical Algorithms - Utilizes statistical and machine learning algorithms to extract insights and predict outcomes.

Secondary Skills

• Communication - Delivers complex technical concepts clearly and effectively to diverse audiences.

• Negotiation & Influence - Builds consensus and drives alignment among stakeholders to achieve business objectives.

• Stakeholder Management - Develops strong relationships with internal and external partners to understand needs and deliver impactful data solutions.

• Proven hands-on experience as a Data Scientist or AI/ML Engineer.

• Strong proficiency in Python and SQL, with a solid grasp of software engineering best practices.

• Experience working within cloud computing environments (e.g., AWS, Azure, GCP).

• Familiarity with Machine Learning Operations (MLOps) frameworks and pipelines.

• Excellent analytical, problem-solving, and critical-thinking abilities.

• Strong communication and presentation skills, with the ability to convey insights effectively to both technical and non-technical audiences.

• High attention to detail and a structured approach to data-driven problem-solving.