Architect, Data Science

Job description

Overview

Main Purpose of the role :

This role has responsibilities where PFNA projects require advancement in Enterprise Data Analytics Platform Capabilities and Solutions. You will be an innovative, curious data professional who helps the organization build more innovative enterprise capabilities in the Data Analytics space by executing efforts towards evaluations, POCs and Pilot for new and existing Technology stacks for the upcoming Enterprise use cases to strengthen the Data Analytics Platform to improve performance, functionality and cost of the Firm’s Data Analytics solutions while adhering to enterprise standards. This role would be shepherding these projects through various IT processes such as Information Security reviews, engaging with Cloud Solution Architects to design / architect a solution, seeking approval for that architecture with the Cloud Architecture / Engineering group or Enterprise Architecture steering committee, bringing together application team members and the partner team members to assess what cloud services will be required, and what the plan would be for building those. In any cloud, there are various services that can be used to solve any business problem. The role is to help shift through the options and select the cloud services that are the most robust and cost effective for the business project at hand, without overengineering a solution and increasing its cost. In this role you will do – validation of Azure cost for the proposed implementation, confirmation of high availability for the solution, validation of disaster recovery requirements / pattern choice, testing of implementation and high availability and disaster recovery (if required), as well as various reviews with Information Security to ensure that PepsiCo data is secured properly, based on data classification. The candidate will focus on working with the various Product and Application Owners to review project needs and ensure to architect, design, implement, and test the requisite cloud services.

Responsibilities

The Data Science Architect will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.
Accountabilities:
- Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
- Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
- Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
- Use big data technologies to help process data and build scaled data pipelines (batch to real time)
- Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines leveraging the NGAA platform (Azure)
- Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
- Automate ML models deployments

Qualifications

Years of Experience:

Total 8 or above years of experience
- Minimum 5 years of work experience data science /Machine learning
- Minimum 3 year of SQL experience
- Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred

 

Mandatory Skills(Technical):

• Programming Skills – Hands-on experience in statistical programming languages like R, Python, and database query languages like SQL. Familiarity with Spark, Hive, Pig is an added advantage.
• Machine Learning – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models
• Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators.
• Deep Learning – Hand on Experience with NLP and NLU application. Good understanding of deep learning models like transformer, BERT and GPT and encode and decoder. Able to create custom deep learning model with Explainability based on need for the business.
• Experience is Exploratory data Analysis for large data set.
• Knowledge of ML Ops / DevOps and deploying ML models is required.
• Experience executing and contributing to ML OPS automation infrastructure is good to have
• Exceptional analytical and problem-solving skills.
• Expertise on Large Language Model preferred with exposure to implementing generative AI using ChatGPT / Open AI and other models. Harvesting Models from open source will be a added advantage

 

 

Mandatory(Non-Technical):  

• Ability to work with virtual teams (remote work locations); lead team of technical resources (employees and contractors) based in multiple locations across geographies
• Participate in ideation and solutioning discussions, driving clarity of complex issues/requirements to build robust solutions
• Prior experience in working in SCALED AGILE framework
• Should be able to work independently

• Strong stakeholder and business collaboration skills
• Strong Written and Oral Communication Skills (Both Business and Technical)
• Seeks to improve processes and strive towards efficiencies over time
• Team player who can incorporate ideas and work towards the best solutions
• Adapt at dealing with ambiguity and working in a matrix environment
• Ability to work with virtual teams (remote work locations)
• Lead / participate in functional and technical discussions, driving clarity of complex issues/requirements to build robust solutions
• Ability to work independently with business partners to understand requirements quickly, perform analysis and determine suitable solutions