Data Scientist - Machine Learning/AI/NLP & Deep Learning

Hiring for a specialized technology-led company that offers solutions for IP management., which is a state-of-the-art software platform helping customers derive more value from their IP, as it optimizes IP management. Their platform and products are also used by expert teams globally to asset manage IP for customers.

With it's offices in Los Altos - CA, Salt Lake City - UT, Seattle - WA, Bangalore and Gurgaon the company with its products and solutions are poised to change the way patent portfolios are managed today by enhancing governance, optimizing costs, bringing operational rigor and offering directional decision support for monetizing IP

Looking for brilliant data engineers to join the data science team in synthesizing and leveraging massive datasets of Intellectual Property data to enhance portfolio development outcomes & value exploration.

This is a unique opportunity to join a new, multidisciplinary team of creative and passionate individuals destined to change the face of IP industry. You will be focused on the company's agenda and lead the high-impact projects utilizing big data analytics, AI and machine learning to improve IP Prosecution & Value analysis.

Responsibilities:

- As a Data Scientist, you will provide expertise on AI & NLP related concepts and inspire the adoption of advanced analytics and data science across the entire breadth of our organization.

- You will actively contribute and develop of one of the modules in AI & ML fuelled data analytics product for IP Asset Management & Monetization.

- You will research, design, implement and validate cutting-edge AI & NLP algorithms to analyze diverse sources of Patent and non-patent literature, market data, and research data to achieve targeted outcomes

- You'll work in the Development centres with a that team comprises of developers, data engineers, product analysts and data scientists based in Bangalore and Los Altos. You will report into the Head of Data Science and work closely with the Engineers, Product Managers and Subject Matter Experts.

Required Qualifications & Essential Background

- Engineering with experience in building multi-tier analytics solutions using python / Java with 6 - 10 years of experience in Programming & working in a Data-intensive environment

- Deep understanding of statistical and predictive modeling concepts, AI & machine-learning approaches, clustering, classification techniques, and recommendation & optimization algorithms.

- Good experience in programming languages such as Python/Java /C++.

- Expertise in Big data platforms like Hadoop, Spark, Kafka etc.

- Expertise in Developing Machine learning and Deep learning models for text data (python Scikit learn, Keras, Tensorflow) to answer business queries

- Expertise Working with large text corpora and using APIs in Natural language processing (NLTK, Spacy, Textblob, OpenNLP, Mallet, cleanup )

- Experience or any knowledge of Intellectual Property prosecution or invention management related businesses would be an added advantage.

- Deep expertise in structured and non-structured databases like SQL, MySQL, MongoDB etc.

- Executed at least data science projects that are into production

Additional skill sets

- Understand, use and create Machine learning models, tune them for performance, improve accuracy.

- Programmed using various Supervised/Unsupervised learning algorithms for sizeable data sets

- Application of Deep learning techniques like RNN, CNN for text, Word/Phrase embeddings

- NLP and Machine learning skills with Python programming using Opensource libraries and frameworks a MUST with a significant hands-on project experience.

- Extensively used algorithms for language/ text processing of documents related to the industry such as - Company Annual reports (BFSI) / Equipment or Device-related documents / Patent texts.

- Data life cycle management- normalizing, profiling, summarizing and plotting data - using tools like open refine and python pandas

- Using REST Services to integrate the models with other applications/dashboards/ datasource.

- Adhering to Software engineering processes using tools like GitHub / BitBucket / SVN