Data Scientist

Job Description:

1. Act as a hands-on problem solver with a consultative mindset, applying Machine Learning & Deep Learning algorithms to address business challenges

a. Leverage expertise across a broad range of AI/ML techniques and algorithms to identify the best combinations for solving the problem

b. Enhance model accuracy to drive greater business impact

c. Assess and estimate business impact resulting from model deployment

2. Collaborate with domain/customer teams to understand business context and data dictionaries, and apply appropriate Deep Learning solutions for the business challenge

3. Use tools and scripts to perform adequate data preprocessing and feature engineering for model development—Python / R / SQL / Cloud data pipelines

4. Design, develop & deploy Deep Learning models using Tensorflow / Pytorch

5. Experience working with Deep Learning models for text, speech, image, and video data

a. Design & develop NLP models for Text Classification, Custom Entity Recognition, Relationship extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, and Semantic Search using NLP tools such as Spacy and open-source Tensorflow, Pytorch, etc

b. Design and develop Image recognition & video analysis models using Deep Learning algorithms and open-source tools such as OpenCV

c. Knowledge of state-of-the-art Deep Learning algorithms

6. Optimize and tune Deep Learning models to achieve the highest possible accuracy

7. Utilize visualization tools/modules to explore and analyze outcomes and support model validation (e.g., Power BI / Tableau)

8. Partner with application teams to deploy models on cloud as a service or on-prem

a. Deploy models within a Test / Control framework for tracking

b. Build CI/CD pipelines for ML model deployment

9. Integrate AI&ML models with other applications using REST APIs and other connector technologies

10. Continuously upskill and stay current with the latest techniques and best practices. Write white papers and create demonstrable assets that summarize the AIML work and its impact.

· Technology/Subject Matter Expertise

· Sufficient expertise in machine learning, mathematical and statistical sciences

· Use of versioning & collaborative tools like Git / Github

· Strong understanding of the AI solutions landscape—cloud, GPU-based compute, data security and privacy, API gateways, microservices-based architecture, big data ingestion, storage and processing, and CUDA Programming

· Ability to evolve prototype-level ideas into scalable, industrial-grade solutions

· Capability to quantify and estimate the impact of ML models

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· Softskills Profile

• Curiosity to think in fresh and unique ways with the intent of breaking new ground.
• Must have the ability to share, explain and 'sell' their thoughts, processes, ideas and opinions, even outside their own span of control
• Ability to think ahead, and anticipate the needs for solving the problem will be important

· Ability to communicate key messages effectively, and articulate strong opinions in large forums

· Desirable Experience:

· Keen contributor to open source communities, and communities like Kaggle

· Ability to process huge amounts of data using Pyspark/Hadoop

· Development & application of Reinforcement Learning

· Knowledge of Optimization/Genetic Algorithms

· Operationalizing Deep learning models for a customer and understanding nuances of scaling such models in real scenarios

· Optimize and tune deep learning models for best possible accuracy

· Understanding of stream data processing, RPA, edge computing, AR/VR etc

· Appreciation of digital ethics and data privacy will be important

· Experience working with AI & Cognitive services platforms like Azure ML, IBM Watson, AWS Sagemaker, and Google Cloud will be a big plus

· Experience with platforms like Data robot, Cognitive scale, H2O.AI etc will be a big plus