Gartner - Senior Data Scientist - NLP/Recommendation Engine - Digital Products & Innovations Group

Data Scientist (Digital Products and Innovations) role:

This is a unique opportunity for a creative and charismatic individual to be a part of a data science research and product development team for Gartner Digital Products and Innovations Group, a fast growing data science organization. Our mission is to drive increasing business impact by building world class data science practices. We are looking for a data scientist to partner with product managers and data engineers to build and launch Machine Learning and Artificial Intelligence Business capabilities related to search and other digital experiences.

Your ability to apply Lean Product Principles to the data science process will both ensure proper ROI and fast iteration of the solution. Just as important is the intuitive ability to move towards the important vs. the interesting. This becomes especially relevant as you lead data science projects to develop the product opportunities we uncover. Having a strong goal-orientation (vs. a task-orientation) and an anchoring on the client experience and business problems will help you navigate the decisions needed to build great products and deliver strong business results.

Sample initiatives:

Given the broad areas that we are responsible for, you will likely to start with a domain but may get involved in other domains. A few potential projects you may get involved:

- Applying Natural Language Processing and predictive modeling to understand clients search query intent and overall interest.

- Using advanced machine learning techniques to develop search ranking and relevance engine.

- Leverage clients digital and nondigital data to personalize and contextualize search experience.

- Develop graphical network of interlinked assets into a Knowledge graph and diversify search results into connected topics and nuggets of research insights.

- Leverage internal and external big data to understand clients company level priorities, competitive landscape and deliver targeted support

Responsibilities:

- Manage multiple Data science projects and work closely with the Data Engineering team and Product owners to deliver high value business capabilities.

- Own at least one important business goal, KPI and related key initiatives.

- Be responsible for high quality data science solutions with respect to accuracy and coverage. Be accountable for solutions scalability, stability and business adoption.

- Help data science team with strong subject matter expertise in at least one of Machine learning, Statistical Modeling and Natural Language Processing.

- Responsible to pitch ideas, present solutions and influence senior leaders with strong business value propositions.

Qualifications:

The ideal candidate will have

- 6-8 years hands-on experience building Machine Learning Models, Natural Language Applications or artificial intelligence business capabilities.

- Bachelors Degree in a quantitative field (math, stat, computer science, engineering, etc.) required; Masters degree is preferred and PhD a strong plus.

- Experience and proficiency with Python coding, Big data technologies like PySpark and important python machine learning libraries and statistical packages.

- Working experience with SQL/relational databases (e.g., Oracle) and NoSQL databases (e.g., MongoDB, Neo4j), Linux and shell scripting.

- Hands on Experience of working in Deep Learning Models and related frameworks like TensorFlow, Keras, PyTorch etc.

- Experience working within cloud computing environment such as Azure or AWS.

- Demonstrated ability to translate quantitative analysis into actionable business strategies.

- Strong working knowledge of Lean product principles, Software development lifecycle, IT and data science/machine learning topics

- Practical, intuitive problem solver with a demonstrated ability to translate business objectives into actionable data science tasks and translate quantitative analysis into actionable business strategies

- Ability to work collaboratively with a variety of business, data science and technical stakeholders

- Ability to work in a culture that thrives on feedback and seeks opportunities to stretch outside comfort zone

- Bias for action and completion