The Lead Data Scientist plays an integral role in the evolution of data insights. This candidate will have an initial focus of driving the department’s natural language processing (NLP) capabilities forward, then expand into general predictive modeling. The candidate will be intricately involved in running analytical experiments and will regularly evaluate alternate models via theoretical approaches. The candidate must understand how to effectively communicate insights gained from these models to leadership in terms of business value.
The candidate should be innovative and forward thinking - developing solutions that can enable the organization to achieve its strategic goals and initiatives.
- Understand our business objectives and processes, and the data that can describe and influence business results
- Develop, implement, and use a broad set of machine-learning models and quantitative techniques for prediction and classification
- Build document clustering, topic analysis, text classification, named entity recognition, sentiment analysis, and part-of-speech tagging methods for unstructured and semi-structured data
- Collaborate with the Product Owner and systems analysts to transform customer needs into actionable insights
- Research and implement new statistical or other mathematical methodologies as needed to design and develop new conceptual models and algorithms
- Devise and test hypotheses that demonstrate causality and correlations between data and business results
- Design and implement statistical analysis and data quality procedures to understand and work around model limitations and to regularly augment and improve the models
- Identify new uses for data in pursuit of enhancements to business effectiveness and efficiency
Supervisory or Management Responsibility:
The Lead Data Scientist would provide oversight and mentoring to Junior Data Scientists, and Machine Learning Engineers.
- Degree in Computer Science, Statistics, Mathematics or related technical field; Master’s required, PhD preferred
- 8+ years of experience in advanced analytics, predictive modeling, machine learning, and solving challenging business problems.
- 5+ years’ experience with relational databases and SQL
- 2+ years’ experience developing natural language processing models, using NLP toolkits such as NLTK, OpenNLP, Stanford CoreNLP etc.
- Significant experience using Semantic analysis (named entity recognition, sentiment analysis)
- Knowledge and understanding of modeling and word representations (TF-IDF, LSA, LDA, word2vec)
- Extensive experience using scikit-learn, TensorFlow or equivalent machine and deep learning libraries
- Extensive proficiency in at least one of the following languages: R, Python, and Scala
- Strong statistical analysis background with a deep understanding of a variety of ML algorithms
OTHER DESIRED EXPERIENCE
- Developing in cloud-based platforms such as Google Cloud Platform, Microsoft Azure (specifically Cognitive Services), or AWS
- Leveraging both structured and unstructured data sources.
- Experience working in a big data environment (e.g. Hadoop, Spark, NoSQL)
- Experience with data visualization tools such as Tableau and Power BI
- Experience with using services or ETL tools to deploy models
- Ability to quickly prototype models with limited and/or ambiguous requirements,
- Ability to translate model results into business value and communicate findings to Executive Leadership, Product Owners, Experience Owners, and other key stakeholders (presenting findings to individuals who are not Data Scientists)
- Willingness to evaluate recent advances in NLP and apply techniques accordingly
- Ability to work in a fast-paced multidisciplinary environment
- Strong written and verbal communication skills
- Ability to advise senior management in strategic decisions – from business use case prioritization to tool/software selection
- Customer Service
- Building Relationships
- Business Knowledge / Organizational Acumen
- Self-Motivation/Self Starter
- Leading Self and Others
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