Responsibilities
- Collect, clean, and analyze complex datasets
- Identify trends, patterns, and actionable insights
- Apply statistical techniques to support data-driven decisions
- Develop and deploy machine learning models to predict future trends and outcomes
- Apply regression, clustering, classification, and advanced modeling techniques
- Build and optimize algorithms such as: Decision Trees Random Forests Neural Networks Gradient Boosting models
- Decision Trees
- Random Forests
- Neural Networks
- Gradient Boosting models
- Engineer and select relevant features to improve model performance
- Fine-tune model parameters and validate predictive accuracy
- Ensure models are scalable and production-ready
- Deploy machine learning models into production environments
- Support real-time decision-making applications
- Monitor model performance and retrain as needed
- Develop dashboards and visualizations using Tableau, Power BI, or Python libraries (Matplotlib, Seaborn, etc.)
- Communicate insights effectively to technical and non-technical stakeholders
- Design and analyze A/B tests
- Conduct hypothesis testing and provide statistical validation
- Measure business impact of changes and enhancements
- Collaborate with IT and database teams to access and integrate data sources
- Work with cross-functional teams (engineering, business analysts, domain experts)
- Align data science initiatives with strategic business objectives
- Ensure ethical data practices and compliance with data privacy regulations
- Maintain documentation and transparency in model development
- Mentor junior data scientists and analysts
- Contribute to best practices and data science methodologies
- Stay current with emerging tools, technologies, and industry trends
Basic qualifications
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field
- 5–10 years of experience in data science, machine learning, and statistical analysis
- Proficiency in Python, R, or Julia
- Strong understanding of machine learning algorithms and their applications
- Experience with SQL and database querying
- Experience with data visualization tools (Tableau, Power BI, or Python libraries)
- Strong analytical, problem-solving, and critical-thinking skills
- Excellent written and verbal communication skills
Preferred qualifications
- Master’s or Ph.D. in a quantitative field
- Experience with big data technologies (Hadoop, Spark)
- Experience with distributed computing frameworks
- Experience deploying models in cloud environments
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Machine Learning Data Science Data Engineer Ai