Sr. Data Scientist

Responsibilities:

• Communicate Advanced Research and Modeling Insights: Translate complex, model-driven findings into actionable insights for technical and non-technical audiences, using storytelling and visualization to clarify sophisticated data science concepts.
• Lead and Execute Data Science Research: Direct and conduct applied research on complex data science problems, utilizing statistical modeling, machine learning, and deep learning techniques to develop innovative solutions. Drive hypothesis generation, model selection, and validation to ensure robustness and relevance of findings.
• Advance Data Science and Modeling Expertise in a Fast-Paced Setting: Stay at the forefront of emerging research and algorithmic techniques, applying the latest advances in machine learning, natural language processing, and statistical modeling to solve business challenges. Rapidly adapt research methods to meet the evolving demands of a dynamic, data-driven environment.
• Innovate with Model Development and Optimization: Address complex problems through advanced model development, selection, and optimization. Leverage a thorough understanding of model evaluation, hyperparameter tuning, and bias mitigation to ensure high-quality, fair, and effective solutions that enhance business outcomes.
• Conduct Research to Drive Strategic Insights: Design and execute rigorous studies that yield strategic insights, integrating research into actionable frameworks. Develop novel methodologies and frameworks that directly inform data-driven decision-making and align with key business priorities.

Basic Requirements:

• On-site working at the ML physical office, 5-days per week required
• Advanced degree (Ph.D. or Master's) plus 4+ years of non-academic professional experience
• Proficiency in Python or R (both preferred)
• SQL expertise
• Strong background in statistical methods, including: Regression analysis, Model testing, Logistic regression, Generalized Linear Models (GLM) and Solid understanding of statistics and random variables
• Technical Knowledge:
• Familiarity with one or more advanced machine learning techniques:
• Gradient Boosting Machines (GBM)
• K-means clustering
• Neural Networks
• Support Vector Machines (SVMs)
• Bayesian Networks
• Natural Language Processing (NLP)
• Experience with machine learning frameworks such as: TensorFlow, Scikit-learn, PyTorch and Keras

Other Characteristics:

• Team player and proven relationship-builder
• Strong interpersonal skills, high level of professionalism and integrity
• Excellent organizational and project management skills
• Experience handling multiple responsibilities, tasks, and projects in a fast-paced environment preferred
• A positive attitude that approaches tasks/projects from a hands-on, roll up your sleeves frame of mind