Key Result Areas and Activities:
- Project Execution & Delivery
- Lead and contribute to the end-to-end execution of data science projects, including data discovery, cleansing, analysis, feature engineering, and model development.
- Design, develop, and deploy AI solutions that address business challenges and deliver measurable value.
- Implement complete AI/ML pipelines—from data collection and preprocessing to model training, evaluation, and deployment.
- Collaboration & Stakeholder Engagement
- Work closely with data scientists, software engineers, and domain experts to understand business requirements and translate them into scalable AI/ML solutions.
- Communicate technical concepts and insights effectively to both technical and non-technical stakeholders.
- Knowledge Sharing & Talent Development
- Share domain expertise in AI/ML across teams and clients, contributing to internal knowledge-building initiatives.
- Support recruitment, mentoring, and coaching efforts to grow talent and build capacity within the AI practice.
- Innovation & Capability Building
- Contribute to the Data Science Center of Excellence (COE) by developing reusable frameworks, templates, and best practices.
- Stay current with emerging trends, technologies, and methodologies in AI/ML and apply them to enhance solution offerings.
Roles & Responsibilities
Roles & Responsibilities:
- Demonstrate practical knowledge of transformer-based models and generative AI, including prompt engineering to optimize model outputs for real world applications.
- Proficient in statistical programming languages such as Python and R, with strong SQL skills for data manipulation and analysis.
- Possess a solid understanding of data architecture, pipeline development, and MLOps practices for robust model lifecycle management.
- Apply advanced statistical methods and machine learning algorithms, including ensemble techniques, deep learning, and NLP.
- Develop reusable frameworks, templates, and governance processes to support scalable and maintainable AI solutions.
- Utilize platforms like SageMaker, AzureML, and Dataiku for feature engineering, model training, and optimization.
- Communicate insights effectively using data visualization tools and align AI solutions with business processes to deliver both qualitative and quantitative value.
Qualifications:
- Bachelor's degree in computer science, engineering, or related field (Master's degree is a plus).
- Demonstrated continued learning through one or more technical certifications or related methods.
- At least 6 years AI/ML experience with a minimum of 4 years in Python, R.
Qualities:
- Assist senior team members in conducting workshops and discovery sessions to understand business priorities, challenges, and focus areas.
- Contribute to various stages of data science projects—from data exploration to model development.
- Work collaboratively with peers and other departments to support project goals, share insights, and learn best practices in data science and analytics.
- Ability to work with teams and clients across time zones
- Self-motivated and focused on delivering results for a fast-growing team and firm
