Data Scientist II 4P/573
Actively Hiring
Full-time Posted 5 months ago
Responsibilities
- check_circle Collect, clean, and analyze complex datasets
- check_circle Identify trends, patterns, and actionable insights
- check_circle Apply statistical techniques to support data-driven decisions
- check_circle Develop and deploy machine learning models to predict future trends and outcomes
- check_circle Apply regression, clustering, classification, and advanced modeling techniques
- check_circle Build and optimize algorithms such as: Decision Trees Random Forests Neural Networks Gradient Boosting models
- check_circle Decision Trees
- check_circle Random Forests
- check_circle Neural Networks
- check_circle Gradient Boosting models
- check_circle Engineer and select relevant features to improve model performance
- check_circle Fine-tune model parameters and validate predictive accuracy
- check_circle Ensure models are scalable and production-ready
- check_circle Deploy machine learning models into production environments
- check_circle Support real-time decision-making applications
- check_circle Monitor model performance and retrain as needed
- check_circle Develop dashboards and visualizations using Tableau, Power BI, or Python libraries (Matplotlib, Seaborn, etc.)
- check_circle Communicate insights effectively to technical and non-technical stakeholders
- check_circle Design and analyze A/B tests
- check_circle Conduct hypothesis testing and provide statistical validation
- check_circle Measure business impact of changes and enhancements
- check_circle Collaborate with IT and database teams to access and integrate data sources
- check_circle Work with cross-functional teams (engineering, business analysts, domain experts)
- check_circle Align data science initiatives with strategic business objectives
- check_circle Ensure ethical data practices and compliance with data privacy regulations
- check_circle Maintain documentation and transparency in model development
- check_circle Mentor junior data scientists and analysts
- check_circle Contribute to best practices and data science methodologies
- check_circle 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
Fulltime Machine Learning Data Science Data Engineer Ai