Role Overview:
- Combine strategic thinking with technical skills in AI, machine learning, and data analytics.
- Implement data-driven solutions aligned with business goals.
- Lead enterprise projects to improve decision-making, solve complex problems, and drive business growth.
- Translate data insights into actionable recommendations with meaningful business impact.
Key Responsibilities:
1. Implement AI, Data Science, and Technical Execution:
- Support the design, implementation, and optimization of AI-driven strategies per stakeholder requirements.
- Design and implement machine learning solutions and statistical models from problem formulation through deployment.
- Apply GenAI, traditional AI, ML, NLP, computer vision, or predictive analytics.
- Collect, clean, and preprocess structured and unstructured datasets.
- Refine data-driven methodologies for transformation projects.
- Utilize cloud platforms to ensure scalability of AI solutions.
- Leverage reusable assets and apply IBM standards for data science and development.
- Apply ML Ops and AI ethics.
2. Strategic Planning & Execution:
- Translate business requirements into technical strategies.
- Ensure alignment to stakeholders strategic and tactical needs.
- Apply business acumen to analyze business problems and develop solutions.
- Collaborate with stakeholders and team to prioritize work.
3. Project Management and Delivering Business Outcomes:
- Manage all stages of AI/data science projects from data exploration to model development to deployment.
- Use agile strategies to manage and execute work.
- Monitor project timelines and resolve technical challenges.
- Design and implement measurement frameworks to benchmark AI solutions and quantify business impact.
4. Communication and Collaboration:
- Communicate regularly and present findings to stakeholders, both technical and non-technical.
- Create compelling data visualizations and dashboards.
- Work with data engineers, software developers, and other team members to integrate AI solutions into existing systems.
Required Education:
Required Technical and Professional Expertise:
Experience:
- Hands-on experience with AI/ML technologies and statistical modeling through coursework, projects, internships, or full-time positions.
- Experience with prompt engineering or fine-tuning LLMs.
- Familiarity with tools like LangChain, Hugging Face Transformers, or OpenAI APIs.
- Understanding of model evaluation metrics specific to LLMs.
Technical Skills:
- Proficiency in Python and SQL for data analysis and ML model development.
- Experience in statistics, machine learning, generative and traditional AI.
- Knowledge of ML algorithms and frameworks: linear regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), neural networks, TensorFlow, PyTorch.
- Familiarity with cloud platforms and data processing frameworks.
- Understanding of large language models (LLMs).
- Familiarity with object-oriented programming.
- Experience with Python libraries: NumPy, Pandas, SciPy, scikit-learn, matplotlib, Seaborn.
- Knowledge of APIs, Docker, Flask, and model serving technologies.
- Experience with Jupyter, Git, AWS, Azure, IBM Cloud.
Strategic and Analytical Skills:
- Strategic thinking and business acumen.
- Strong problem-solving abilities and eagerness to learn.
- Ability to work with datasets and derive insights.
- Attention to detail.
Communications and Soft Skills:
- Excellent communication skills for explaining technical concepts.
- Independent and team-oriented.
- Understands AI ethics principles.
- Inclusive, adaptable to fast-paced environments.
- Enthusiastic about learning and applying new technologies.
- Growth mindset and ability to balance multiple initiatives, prioritize tasks, and meet deadlines.
