Principal Data Scientist, Philadelphia, PA, USA
Candidates must be able to work in the USA unrestricted in order to apply for this role)
Salary : $175,000 – $200,000 USD + 5% company bonus + superb benefits.
Benefits include: Medical Insurance, Dental Insurance, Vision Insurance, Life Insurance, Retirement Scheme, Paid Time Off
Our client is a global leader, in Analytics and Insights, with more than 3,500 employees in over 100 countries. They are looking for a Principle Data Scientist to become a new addition to their established team of data scientists in Philly and be responsible for leading projects at enterprise level.
The team you would be working in is key in developing best in class algorithmic services and search platforms for the company’s range of products, using Machine Learning, NLP, and Information Retrieval to solve problems along the entire Lifecycle of Innovation including algorithms to classify content, automating content extraction workflows, extracting and resolving entities, building recommender and decision support systems, predicting risk and outcomes, and enabling unique ways of finding content in order to lead innovation across the world.
You must be an experienced principal-level applied scientist as you will lead large scale, complex data science projects by working closely with product managers and business partners across the company in order to identify opportunities to innovate and build game-changing product features using Machine Learning, Information Retrieval, NLP and Predictive Analytics. You will own one or more research areas, represent the team in executive-level meetings, mentor other scientists and engineers in the team, and lead the research and delivery of models in close collaboration with scientists and engineers.
The ideal candidate will have a deep technical background along with mentorship and leadership experience, and a proven track record of proposing innovative features and platforms, securing funding for their ideas, and delivering significant, quantifiable business impact. You will also have exceptional interpersonal and negotiation skills, ability to earn trust of others, and ability to move fast and make effective decisions in the presence of significant ambiguity.
Essential Experience & Qualifications required:
- Masters degree in Computer Science, Information Systems, Statistics, Mathematics, Engineering or a related field.
- Excellent understanding of ML, NLP, Deep Learning, and statistical methodologies.
- Excellent programming skills (Java/Python/R/SaS) and understanding of computational complexity and data structures.
- 7+ years proven track record in the application of ML and NLP in an industrial setting.
- Excellent analytical and quantitative skills and strong bias towards data-driven decision making.
- Proven ability to understand complex business problems, identifying opportunities for ML / AI and launching models in production.
- Experience owning deliverables for machine learning, analytics or large-scale data-driven product features.
- Ability to understand and discuss architectural concepts, tradeoffs and new opportunities with technical team members.
- Strong communication and collaboration skills, with a proven ability to work across functions and influence senior leaders.
- Ability to comfortably and confidently present to all levels and to work with both technical and non-technical stakeholders across multiple business units including at the executive level.
- Proven track record of innovating on behalf of customers and using metrics to prove new ideas and to drive prioritization and funding decisions.
- Established history of building successful, large-scale products from scratch.
- Ability to test ideas and adapt methods quickly end to end from data extraction to implementation and validation
Preferred but not essential skills/experience:-
- Experience working in Agile enabled teams
- PhD in Machine Learning would be perfect but not essential
- Familiarity with academic research, patents, trademarks or life sciences data sets
- Experience with search engines, classification algorithms, recommendation systems, and relevance evaluation methodologies a plus.