About
AshStevens
Data Science andMachine Learning
About
Hi, I'm Ash! As a data scientist with a background in psychology and research, I bring a unique understanding of human behaviour to my work in AI and machine learning. My work centers around applying best practice machine learning techniques while keeping people at the heart of every decision. I collaborate closely with stakeholders to deeply understand the challenges they face, ensuring we tackle the root of the problem together. With strong technical skills and a focus on clear, effective communication, coupled with an intense curiosity, I bring a holistic approach to data science that drives results and fosters meaningful solutions.
Skills
My Expertise
What can I do?
Skills
1 - Machine Learning & Artificial Intelligence
- Supervised & Unsupervised Learning: Proficiency in algorithms like regression, classification, clustering, dimensionality reduction & tree based models.
- Deep Learning: Experience with neural networks using frameworks like TensorFlow or PyTorch.
- Model Evaluation & Tuning: Techniques for cross-validation, hyperparameter tuning, and assessing model performance.
- Natural Language Processing & LLM's: Knowledge in text & sentiment analysis, langchain, and agent building.
3 - Programming & Automation
- Python Programming: Advanced proficiency in Python for programming & machine learning.
- Deployment & Monitoring: Deploying machine learning models into production environments and monitoring their performance over time.
- Cloud Infrastructure: Leveraging cloud platforms (Google Cloud, AWS, Azure) and terraform to manage scalable workflows and optimize resource usage.
- Model Maintenance: Ensuring that deployed models remain accurate and relevant, with periodic retraining and tuning.
2 - Communication & Interpersonal Skills
- Stakeholder Collaboration: Ability to work with cross-functional teams, translating complex technical concepts into business language.
- Data Storytelling: Crafting compelling narratives from data insights using visualizations or dashboards (e.g., Looker).
- Problem-Solving & Strategy: Identifying core business problems, formulating data-driven solutions, and advising on AI/ML-driven business strategy.
- Ethics & Explainability in AI: Understanding of ethical considerations in AI, transparency in models, and ensuring human-centered decision-making processes.
4 - Data Manipulation & Analysis
- Data Wrangling: Cleaning, transforming, and preparing data using tools like pandas, SQL or dbt.
- Exploratory Data Analysis (EDA): Using statistical methods, visualizations, and hypothesis testing to understand datasets and extract insights.
- Feature Engineering: Crafting and selecting meaningful features for machine learning models.
- Time Series Analysis: Working with time-based data, forecasting, and analyzing trends and patterns.