Compunnel, Inc.
We are seeking an experienced Senior Data Scientist to join our Enterprise Data and Analytics team.
In this role, you will leverage machine learning, segmentation, and statistical inference on large datasets to uncover insights that improve customer understanding and drive business decisions.
You will lead initiatives to model complex business problems, develop predictive solutions, and support senior leadership with actionable analytics.
Key Responsibilities
Apply advanced analytics methods to extract value from business data. Design and execute large-scale experiments and build data-driven models to address business questions. Conduct research on cutting-edge techniques in machine learning, deep learning, and AI. Define requirements for training and evolving deep learning models and algorithms. Develop and communicate a vision and roadmap for leveraging data as a strategic asset. Present data-based recommendations to product teams and stakeholders. Own the end-to-end model development process: requirements gathering, data sourcing, model fitting, presenting results, and production deployment. Required Qualifications
Master’s degree in Computer Science, Statistics, Economics, or related field. 1–3 years of experience in machine learning, cloud computing, and statistical analysis. Strong knowledge of predictive modeling methodologies and statistical techniques (e.g., Bayesian inference, regression, hierarchical models). Proficiency in Python, R/RStudio, SAS, SQL, and NoSQL. Experience with Cloud ML technologies (e.g., AWS SageMaker) and ML frameworks (e.g., TensorFlow, scikit-learn, caret). Ability to work with both structured and unstructured data sources. Strong communication skills for presenting complex results to technical and non-technical audiences. Preferred Qualifications
Financial Services industry experience. Familiarity with big data tools and scalable data pipelines. Experience with web scraping and advanced data engineering techniques.
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Apply advanced analytics methods to extract value from business data. Design and execute large-scale experiments and build data-driven models to address business questions. Conduct research on cutting-edge techniques in machine learning, deep learning, and AI. Define requirements for training and evolving deep learning models and algorithms. Develop and communicate a vision and roadmap for leveraging data as a strategic asset. Present data-based recommendations to product teams and stakeholders. Own the end-to-end model development process: requirements gathering, data sourcing, model fitting, presenting results, and production deployment. Required Qualifications
Master’s degree in Computer Science, Statistics, Economics, or related field. 1–3 years of experience in machine learning, cloud computing, and statistical analysis. Strong knowledge of predictive modeling methodologies and statistical techniques (e.g., Bayesian inference, regression, hierarchical models). Proficiency in Python, R/RStudio, SAS, SQL, and NoSQL. Experience with Cloud ML technologies (e.g., AWS SageMaker) and ML frameworks (e.g., TensorFlow, scikit-learn, caret). Ability to work with both structured and unstructured data sources. Strong communication skills for presenting complex results to technical and non-technical audiences. Preferred Qualifications
Financial Services industry experience. Familiarity with big data tools and scalable data pipelines. Experience with web scraping and advanced data engineering techniques.
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