Data Science Engineer

About Exela

Exela Technologies, Inc. (“Exela”) is a location agnostic, global business process automation ("BPA") leader combining industry-specific and industry-agnostic enterprise software and solutions with decades of experience. Our BPA suite of solutions are deployed in banking, healthcare, insurance and other industries to support mission critical environments. Exela is a leader in workflow automation, attended and unattended cognitive automation, digital mail rooms, print communications, and payment processing with deployments

across the globe. Exela partners with customers to improve user experience and quality through operational efficiency. Exela serves over 3,700 customers across more than 50 countries, through a secure, cloud-enabled global delivery model. We are 22,000 employees strong across the Americas, Europe and Asia. Our customer list includes 60% of the Fortune® 100, along with many of the world’s largest retail chains, banks, law firms, healthcare insurance payers and providers and telecom companies.

Why Exela?

A global, public company (Nasdaq: XELA), the people behind Exela are as important as the company itself. Our

The team's extensive experience across multiple industry verticals gives us a better sense of our clients' needs. That

begins with teams comprised of individuals from diverse backgrounds with different perspectives. Join our global team as we create advancements in business process automation solutions that impact our client’s mission-critical operations across the industries they serve. The diversity of our workforce and their inspiring ideas resonate throughout all that we do – don’t just read about digital innovation, be part of the revolution! We are seeking a highly skilled and experienced Senior Lead Engineer to join our dynamic team. In this role, you will play a crucial part in designing, implementing, and supporting F5 Networks' solutions for our internal / external clients.

Designation:

Experience:

Desired Skills and Experience:

Data Science Engineer

Key Responsibilities:

1. Data Engineering

   :

• Develop and maintain ETL pipelines to pre-process and clean data from various sources.
• Design and optimize data storage solutions for analytics and machine learning use cases.
• Collaborate with data engineers to ensure seamless data flow.

1. Machine Learning & Analytics

   :

• Develop, train, and deploy machine learning models to solve business challenges.
• Conduct exploratory data analysis (EDA) to derive actionable insights.
• Monitor and retrain models to ensure performance and relevance over time.

1. Production Deployment

   :

• Design scalable, reliable, and efficient systems to deploy machine learning models into production environments.
• Implement APIs or integrate models into existing business workflows.

1. Collaboration & Communication

   :

• Work closely with product managers, data analysts, and business stakeholders to align technical solutions with business objectives.
• Present findings, insights, and solutions to both technical and non-technical audiences.
• Continuous Improvement:
• Stay up-to-date with advancements in data science, machine learning, and big data technologies.
• Conduct peer code reviews and ensure adherence to best practices.

Required:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
• 5+ years of experience in data science, machine learning, or related roles.
• Experience in

  Healthcare domain

  project is must.
• Proficiency in programming languages such as Python, R, or Java.
• Hands-on experience with data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
• Expertise in SQL and working with relational databases.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes).
• Familiarity with version control tools (e.g., Git).
• Knowledge of containerization tools like Docker and orchestration tools like Kubernetes is a plus.
• Familiarity with CI/CD pipelines and version control systems like Git.
• Understanding of data governance, security, and compliance requirements.
• Experience with big data technologies (e.g., Spark, Hadoop).
• Knowledge of deep learning techniques and natural language processing (NLP).
• Strong understanding of MLOps and model lifecycle management.
• Proficiency in data visualization tools (e.g., Tableau, Power BI, Matplotlib).