Data Scientist (Python, ML, SQL)

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.