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About the Role

We are looking for an analytical and detail-oriented Data Scientist with deep expertise in graph analytics, network analysis, and scalable data engineering. This is not a conventional machine learning role — the focus is on understanding complex relationships, structures, and patterns within large-scale datasets using graph-based methods and statistical modeling. You will work in a collaborative, fast-paced environment and be expected to contribute across the full data lifecycle — from raw data exploration through to production-grade analytical systems.

This is a full-time, in-office role based out of our Pune office (5 days a week).

Key Responsibilities

Graph Analytics & Network Analysis

• Design and implement graph-based models to identify patterns, clusters, communities, and relationships within complex datasets.

• Apply network analysis techniques using metrics such as clustering coefficient, degree assortativity, density, Gini index, and small-world index.

• Perform multi-hop network traversals and community detection using algorithms such as Louvain partitioning and similar graph clustering approaches.

• Build and query graph databases (Neo4j, ArangoDB) using Cypher Query Language to extract structural insights from connected data.

• Leverage GPU-accelerated graph libraries (cuGraph) to scale graph computations across large datasets efficiently.

Data Engineering & Pipeline Development

• Conduct thorough Exploratory Data Analysis (EDA) on large-scale structured and semi-structured datasets to surface quality issues, distributions, and key features.

• Build, optimize, and maintain scalable data pipelines and stored procedures across cloud data platforms such as BigQuery, PostgreSQL, or Hive.

• Automate data workflows using orchestration tools such as Apache Airflow or Kubeflow Pipelines.

• Apply GPU-accelerated computing (CUDA, CuPy, cuDF) to optimize processing performance on high-volume data workloads.

• Ensure data integrity, reproducibility, and documentation across all analytical workflows.

Statistical Modeling & Insight Generation

• Apply statistical modeling techniques to detect behavioral anomalies, trends, and patterns within datasets.

• Use time series analysis to identify temporal patterns and changes in data over time.

• Translate analytical findings into clear, actionable insights for both technical and non-technical stakeholders.

• Build dashboards and reports using visualization tools (e.g., Trino Superset) to communicate results effectively.

Collaboration & Documentation

• Work closely with product, engineering, and business teams to understand requirements and deliver relevant analytical solutions.

• Contribute to internal knowledge sharing through workshops, documentation, and peer reviews.

• Maintain well-documented codebases and analytical frameworks for long-term maintainability.

Required Skills & Qualifications

Must-Have

• 3+ years of experience in a Data Science, Data Analytics, or Graph Analytics role.

• Strong proficiency in Python with hands-on experience using NumPy, Pandas, CuPy, and cuDF.

• Practical experience with graph analytics libraries — NetworkX, cuGraph, Neo4j, or ArangoDB.

• Solid understanding of graph theory concepts: community detection, network metrics, graph traversal, and clustering algorithms.

• Proficiency in SQL; experience with BigQuery, PostgreSQL, MS SQL, Hive, or similar databases.

• Experience with GPU-based computing using CUDA for performance-critical data tasks.

• Strong analytical thinking and ability to work independently on ambiguous, open-ended problems.

Good to Have

• Experience with Cypher Query Language for querying graph databases (Neo4j / ArangoDB).

• Familiarity with graph ML frameworks such as PyTorch Geometric.

• Exposure to workflow orchestration tools — Apache Airflow or Kubeflow Pipelines.

• Knowledge of cloud data tools such as Trino, MinIO, or IBM Datastage.

• Basic scripting skills in Bash or C++ for automation or performance tasks.

• Experience presenting data insights to senior stakeholders or cross-functional teams.

• Research publications, patents, or open-source contributions in graph analytics or data science are a strong plus.

What We Offer

• Opportunity to solve high-impact, large-scale data problems using modern graph and analytics tools.

• Exposure to cutting-edge technologies including GPU-accelerated computing and graph ML.

• Collaborative, intellectually stimulating work environment with a strong engineering culture.

• Competitive compensation with performance-based incentives.

• Learning & development support — certifications, courses, and conference participation.

• Centrally located Pune office with a structured, in-person team culture.

Core Responsibilities:

• The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
• Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
• Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
• Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
• Continuous Improvement: Identify areas for improvement in model performance and systems.

Skills:

• Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
• Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph
• Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
• Knowledge of model monitoring and performance evaluation.

Required experience:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements
• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Skills: Aws, Aws Cloud, Amazon Redshift, Eks

Must-Haves

Amazon SageMaker, AWS Cloud Infrastructure (S3, EC2, Lambda), Docker and Kubernetes (EKS, ECS), SQL, AWS data (Redshift, Glue)

Skills : Machine Learning, MLOps, AWS Cloud, Redshift OR Glue, Kubernetes, Sage maker

******

Notice period - 0 to 15 days only 

Location : Pune & Hyderabad only

MUST-HAVES: 

• Machine Learning + Aws + (EKS OR ECS OR Kubernetes) + (Redshift AND Glue) + Sage maker
• Notice period - 0 to 15 days only 
• Hybrid work mode- 3 days office, 2 days at home

SKILLS: AWS, AWS CLOUD, AMAZON REDSHIFT, EKS

ADDITIONAL GUIDELINES:

• Interview process: - 2 Technical round + 1 Client round
• 3 days in office, Hybrid model. 

CORE RESPONSIBILITIES:

• The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
• Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
• Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
• Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
• Continuous Improvement: Identify areas for improvement in model performance and systems.

SKILLS:

• Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
• Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaos search logs, etc. for troubleshooting; Other tech touch points are Scylla DB (like BigTable), OpenSearch, Neo4J graph
• Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
• Knowledge of model monitoring and performance evaluation.

REQUIRED EXPERIENCE:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sage maker pipeline with ability to analyze gaps and recommend/implement improvements
• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Job Details

- Job Title: ML Engineer II - Aws, Aws Cloud

- Industry: Technology

- Domain - Information technology (IT)

- Experience Required: 6-12 years

- Employment Type: Full Time

- Job Location: Pune

- CTC Range: Best in Industry

Job Description:

Core Responsibilities:

? The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency

? Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.

? Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.

? Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.

? System Integration: Integrate models into existing systems and workflows.

? Model Deployment: Deploy models to production environments and monitor performance.

? Collaboration: Work closely with data scientists, software engineers, and other stakeholders.

? Continuous Improvement: Identify areas for improvement in model performance and systems.

Skills:

? Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).

? Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph

? Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.

? Knowledge of model monitoring and performance evaluation.

Required experience:

? Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements

? AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in

ML workflows

? AWS data: Redshift, Glue

? Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Skills: Aws, Aws Cloud, Amazon Redshift, Eks

Must-Haves

Aws, Aws Cloud, Amazon Redshift, Eks

NP: Immediate – 30 Days

EXPERTISE AND QUALIFICATIONS

• 14+ years of experience in Software Engineering with at least 6+ years as a Lead Enterprise Architect preferably in a software product company
• High technical credibility - ability to lead technical brainstorming, take decisions and push for the best solution to a problem
• Experience in architecting Microservices based E2E Enterprise Applications
• Experience in UI technologies such as Angular, Node.js or Fullstack technology is desirable
• Experience with NoSQL technologies (MongoDB, Neo4j etc.)
• Elastic Search, Kibana, ELK, Logstash.
• Good understanding of Kafka, Redis, ActiveMQ, RabbitMQ, Solr etc.
• Exposure in SaaS cloud-based platform.
• Experience on Docker, Kubernetes etc.
• Experience in planning, designing, developing and delivering Enterprise Software using Agile Methodology
• Key Programming Skills: Java, J2EE with cutting edge technologies
• Hands-on technical leadership with proven ability to recruit and mentor high performance talents including Architects, Technical Leads, Developers
• Excellent team building, mentoring and coaching skills are a must-have
• A proven track record of consistently setting and achieving high standards

Five Reasons Why You Should Join Zycus

1. Cloud Product Company: We are a Cloud SaaS Company, and our products are created by using the latest technologies like ML and AI. Our UI is in Angular JS and we are developing our mobile apps using React.

2. A Market Leader: Zycus is recognized by Gartner (world’s leading market research analyst) as a Leader in Procurement Software Suites.

3. Move between Roles: We believe that change leads to growth and therefore we allow our employees to shift careers and move to different roles and functions within the organization

4. Get a Global Exposure: You get to work and deal with our global customers.

5. Create an Impact: Zycus gives you the environment to create an impact on the product and transform your ideas into reality. Even our junior engineers get the opportunity to work on different product features.

About Us

Zycus is a pioneer in Cognitive Procurement software and has been a trusted partner of choice for large global enterprises for two decades. Zycus has been consistently recognized by Gartner, Forrester, and other analysts for its Source to Pay integrated suite. Zycus powers its S2P software with the revolutionary Merlin AI Suite. Merlin AI takes over the tactical tasks and empowers procurement and AP officers to focus on strategic projects; offers data-driven actionable insights for quicker and smarter decisions, and its conversational AI offers a B2C type user-experience to the end-users.

Zycus helps enterprises drive real savings, reduce risks, and boost compliance, and its seamless, intuitive, and easy-

to-use user interface ensures high adoption and value across the organization.

Start your #CognitiveProcurement journey with us, as you are #MeantforMore

Design, develop and support real time data monitoring application and a dashboard for a VoIP network

Roles and Responsibilities:

• Design, build and maintain efficient, reusable, and reliable Ruby code
• Ensure the best possible performance, quality, and responsiveness of the applications
• Identify bottlenecks and bugs, and devise solutions to these problems

Required Skills:

• 3+ yrs of experience developing Ruby applications on Linux platform with exposure to HTML, CSS and javascripts
• Solid understanding of object-oriented programming
• Experience with any one NoSQL solution like Redis, MongoDB, CouchDB is a must
• Deep understanding of high traffic, highly scalable, complex web applications
• Ability to work in a dev-automation environment with some source control, continuous integration/delivery systems
• Good problem solving/analytical skills
• Excellent written and verbal communication

Preferred Skills:

• Conversant with Elasticsearch, Neo4j and D3.js
• Inclination to GO programming
• Experience working with open source projects