Practice Lead – Data Scientist
As a Data Scientist, you will conceive, evaluate and improve Saxon’s AI/ML products. You will collaborate with a multi-disciplinary team of engineers and analysts on a wide range of problems. This position will bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of the end user.
8 years plus related work experience or equivalent
- Data Management and Analytics – AI/ML Solution Lead to deepen company’s offerings in design and development of advanced AI/ML data solutions across industry verticals, health care etc. leveraging cloud/hybrid platforms.
- Should play an important role in defining vision, strategy, roadmap, and capabilities of the data science initiative.
- Must act as an internal consultant to the business, delivery and should execute various AI projects which includes conceptualize, tools selection, architect, build and deploy highly scalable AI/ML platform and competency.
Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive knowledge of Saxon industry specific accelerators data structures and metrics, advocating for changes where needed for product development.
- Interact cross-functionally, making business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
- Research and develop analysis, forecasting, and optimization methods to improve the quality of industry vertical facing products or accelerators.
Minimum qualifications: Master’s degree in a quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Physics, Electrical Engineering) or equivalent practical experience.
- 8 plus years of work experience in data analysis related field.
- Experience with statistical software (e.g., R, Python, MATLAB, pandas) and database languages (e.g., SQL)
- Must have Data Science AI/ML experience in Healthcare, other domains are a plus
- Snowflake, Databricks, and Open-Source technologies (Spark SQL, Python, Hive, Scala scripting)
- Experience with leading ETL tools, such as Talend, Informatica, Data Stage or equivalent
- Experience with leading BI Reporting Tools, such as Power BI and Tableau
- Expert in various Data Management technologies/Platforms (NoSQL, Distributed Processing, In-Memory databases, Cloud Data Platforms)
- Hands on experience with Big Data technologies (Spark, Kafka, Hive etc.)
- Knowledge of ML/AI tools and their integration with Big Data environment would be a great advantage.
- Knowledge in ML solutions by gathering Machine Learning needs, conferring with end customers/Data &Analytics team members, understanding data infrastructure and architecture, and work processes.
- Able to understand of Machine Learning principles including standard algorithms for Regression and Classification, Deep Learning constructs (RNN, CNN, RBMs, Auto Encoders, GANs) and AI systems such as Vision, Voice to Text, NLP/NLU and Recommender systems
- PhD degree in a quantitative discipline.
- 8 plus years of relevant work experience, including expertise with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
- Applied experience with machine learning on large datasets.
- Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
- Demonstrated leadership and self-direction. Willingness to both teach others and learn new techniques.
- Demonstrated skills in selecting the right statistical tools given a data analysis problem. Effective written and verbal communication skills.
- Hands on experience with architecting and executing data consolidation with disparate data sources and consolidate them into an Integrated Data Warehouse.
- Strong programming experience in diverse Data technologies with special focus on Synapse Analytics platform
- Hands on experience over the full systems development lifecycle for multiple data warehouse and data lake projects including implementation experience with prevalent industry methodologies.
- Proven ability to analyse requirements and clearly communicates those requirements to both business and technical stakeholders in many forms such as verbally, road maps, presentations, and solution technical documentation such high level and detailed technical design documents.
- Strong Communication: Actively listen to others to understand their perspective and ensure continuous understanding regardless of communication channel or audience.
- Experience in developing solutions using MS Data Platform – Azure Data Factory, Azure Data ware house, Azure Data lake, MDM(Profisee), Dataverse, Power BI, Data Science/AI
- Hands on experience with database design and database optimization techniques for performance in very large volume data warehouse environment using industry standard RDBMS (Oracle, Netezza, Snowflake, Vertica or equivalent)
- Must have experience in cloud platforms (Azure, AWS and GCP etc)