Icon
Data Scientist – Predictive Modeling
A growing technology-driven organization is seeking a skilled and experienced Data Scientist with a strong focus on predictive modeling to join its analytics team. This is a high-impact role where you will design and deliver predictive models that drive strategic business decisions and core applications. The ideal candidate will bring deep expertise in Python, SQL, and modern machine learning frameworks, as well as the ability to tackle complex, ambiguous challenges with precision and creativity.
Key Responsibilities:
- Model Development: Design, build, and rigorously validate predictive models using structured and unstructured data.
- Machine Learning Implementation: Apply advanced techniques using Python libraries such as PyTorch, TensorFlow, and scikit-learn to develop scalable solutions.
- Data Engineering: Perform data wrangling, transformation, and feature engineering using Python and SQL.
- Exploratory Analysis: Conduct thorough exploratory data analysis within Jupyter Notebooks to inform model development.
- Business Problem Solving: Translate complex business challenges into actionable data science projects.
- Autonomous Research: Independently analyze large datasets to uncover trends and actionable insights.
- Cross-functional Collaboration: Work closely with product, engineering, and business stakeholders to ensure alignment with organizational goals.
- Documentation & Communication: Produce clear documentation and present findings to both technical and non-technical audiences.
Required Qualifications:
- Experience: 4+ years of experience in a Data Science role, with a strong track record of building and deploying predictive models in production environments.
- Programming: Expert-level proficiency in Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow).
- Databases: Advanced SQL skills for querying and transforming large-scale datasets.
- Tools: Proficiency in Jupyter Notebooks for data exploration and model development.
- Data Preparation: Expertise in data preprocessing, feature engineering, and cleaning.
- Machine Learning Knowledge: Strong understanding of supervised and unsupervised learning techniques.
- Project Leadership: Ability to independently manage full lifecycle of data science projects.
- Problem Solving: Exceptional analytical skills and the ability to navigate ambiguity.
Preferred Qualifications:
- Production Deployment: Experience deploying machine learning models in production environments.
- Version Control: Familiarity with Git or similar version control systems.
- Education: Advanced degree (Master’s or Ph.D.) in Data Science, Computer Science, Statistics, Mathematics, or a related field.
Source ⇲
