Data Scientist

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:

• Bachelor's Degree

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.