Director Data Science

Responsibilities: 

• Improve robustness of Leena AI current NLP stack 
• Increase zero shot learning capability of Leena AI current NLP stack 
• Opportunity to add/build new NLP architectures based on requirements 
• Manage End to End lifecycle of the data in the system till it achieves more than 90% accuracy 
• Manage a NLP team 

Page BreakRequirements: 

• Strong understanding of linear algebra, optimisation, probability, statistics 
• Experience in the data science methodology from exploratory data analysis, feature engineering, model selection, deployment of the model at scale and model evaluation 
• Experience in deploying NLP architectures in production 
• Understanding of latest NLP architectures like transformers is good to have 
• Experience in adversarial attacks/robustness of DNN is good to have 
• Experience with Python Web Framework (Django), Analytics and Machine Learning frameworks like Tensorflow/Keras/Pytorch. 

Azure – Data Engineer

• At least 2 years hands on experience working with an Agile data engineering team working on big data pipelines using Azure in a commercial environment.
• Dealing with senior stakeholders/leadership
• Understanding of Azure data security and encryption best practices. [ADFS/ACLs]

Data Bricks –experience writing in and using data bricks Using Python to transform, manipulate data.

Data Factory – experience using data factory in an enterprise solution to build data pipelines. Experience calling rest APIs.

Synapse/data warehouse – experience using synapse/data warehouse to present data securely and to build & manage data models.

Microsoft SQL server – We’d expect the candidate to have come from a SQL/Data background and progressed into Azure

PowerBI – Experience with this is preferred

Additionally

• Experience using GIT as a source control system
• Understanding of DevOps concepts and application
• Understanding of Azure Cloud costs/management and running platforms efficiently

Primary Skills

 6 to 8 years of relevant work experience in ETL tools

 Good knowledge working in AWS Cloud Data Bases like Aurora DB and ecosystem and tools (AWS DMS)

Migrating databases to AWS Cloud would be Mandatory

Sound knowledge of SQL and procedural language.

Possess solid experience of writing complex SQL queries and optimizing SQL query performance

Knowledge of data ingestion one-off feed, change data capture, incremental batch

Additional Skills :

Experience in Unix/Linux systems and writing shell scripts would be nice to have

Java knowledge would be an added advantage

Knowledge in Spark Python for building ETL pipelines on cloud would be preferable

• Implement and deliver a product in the healthcare domain using machine learning and AI capabilities
• Ability to work on challenging tasks
• Ability to research ways of doing things efficiently
• Keen approach to problem solving
• Strong experience in statistics, python, SQL, NLP and predictiv modeling

• Bachelors Degree with a total of 9+ years of experience in software development with 2-3 years experience in Data Science
• Experienced in working with large and multiple datasets, data warehouses and ability to pull data using relevant programs and coding
• Well versed with necessary data preprocessing and feature engineering skills
• Background in healthcare IT space will be preferred

Key Responsibilities:

• Partnering with clients and internal business owners (product, marketing, edit, etc.) to understand needs and develop models and products for Kaleidofin business line.
• Good understanding of the underlying business and workings of cross functional teams for successful execution
• Design and develop analyses based on business requirement needs and challenges.
• Leveraging statistical analysis on consumer research and data mining projects, including segmentation, clustering, factor analysis, multivariate regression, predictive modeling, hyperparameter tuning, ensembling etc.
• Providing statistical analysis on custom research projects and consult on A/B testing and other statistical analysis as needed. Other reports and custom analysis as required.
• Identify and use appropriate investigative and analytical technologies to interpret and verify results.
• Apply and learn a wide variety of tools and languages to achieve results
• Use best practices to develop statistical and/ or machine learning techniques to build models that address business needs.
• Collaborate with the team to improve the effectiveness of business decisions using data and machine learning/predictive modeling.
• Innovate on projects by using new modeling techniques or tools.
• Utilize effective project planning techniques to break down complex projects into tasks and ensure deadlines are kept.
• Communicate findings to team and leadership to ensure models are well understood and incorporated into business processes.

Skills:

• 2+ year experience in advanced analytics, model building, statistical modeling, optimization, and machine learning algorithms.
• Machine Learning Algorithms: Crystal clear understanding, coding, implementation, error analysis, model tuning knowledge on Linear Regression, Logistic Regression, SVM, shallow Neural Networks, clustering, Decision Trees, Random forest, Boosting trees, Recommender Systems, ARIMA and Anomaly Detection. Feature selection, hyper parameters tuning, model selection and error analysis, ensemble methods.
• Strong with programming languages like Python and data processing using SQL or equivalent and ability to experiment with newer open source tools
• Experience in normalizing data to ensure it is homogeneous and consistently formatted to enable sorting, query and analysis.
• Experience designing, developing, implementing and maintaining a database and programs to manage data analysis efforts.
• Experience with big data and cloud computing viz. Spark, Hadoop (MapReduce, PIG, HIVE)
• Experience in risk and credit scoring domains preferred