Ab>initio, Big Data, Informatica, Tableau, Data Architect, Cognos, Microstrategy, Healther Business Analysts, Cloud etc.

Review Criteria

• Strong Data Scientist/Machine Learnings/ AI Engineer Profile
• 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
• Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
• Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
• Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
• Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
• Preferred (Company) – Must be from product companies

Job Specific Criteria

• CV Attachment is mandatory
• What's your current company?
• Which use cases you have hands on experience?
• Are you ok for Mumbai location (if candidate is from outside Mumbai)?
• Reason for change (if candidate has been in current company for less than 1 year)?
• Reason for hike (if greater than 25%)?

Role & Responsibilities

• Partner with Product to spot high-leverage ML opportunities tied to business metrics.
• Wrangle large structured and unstructured datasets; build reliable features and data contracts.
• Build and ship models to:
• Enhance customer experiences and personalization
• Boost revenue via pricing/discount optimization
• Power user-to-user discovery and ranking (matchmaking at scale)
• Detect and block fraud/risk in real time
• Score conversion/churn/acceptance propensity for targeted actions
• Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
• Design and run A/B tests with guardrails.
• Build monitoring for model/data drift and business KPIs

Ideal Candidate

• 2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
• Proven, hands-on success in at least two (preferably 3–4) of the following:
• Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
• Fraud/risk detection (severe class imbalance, PR-AUC)
• Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
• Propensity models (payment/churn)
• Programming: strong Python and SQL; solid git, Docker, CI/CD.
• Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
• ML breadth: recommender systems, NLP or user profiling, anomaly detection.
• Communication: clear storytelling with data; can align stakeholders and drive decisions.

Senior Data Engineer Job Description

Overview

The Senior Data Engineer will design, develop, and maintain scalable data pipelines and

infrastructure to support data-driven decision-making and advanced analytics. This role requires deep

expertise in data engineering, strong problem-solving skills, and the ability to collaborate with

cross-functional teams to deliver robust data solutions.

Key Responsibilities

Data Pipeline Development: Design, build, and optimize scalable, secure, and reliable data

pipelines to ingest, process, and transform large volumes of structured and unstructured data.

Data Architecture: Architect and maintain data storage solutions, including data lakes, data

warehouses, and databases, ensuring performance, scalability, and cost-efficiency.

Data Integration: Integrate data from diverse sources, including APIs, third-party systems,

and streaming platforms, ensuring data quality and consistency.

Performance Optimization: Monitor and optimize data systems for performance, scalability,

and cost, implementing best practices for partitioning, indexing, and caching.

Collaboration: Work closely with data scientists, analysts, and software engineers to

understand data needs and deliver solutions that enable advanced analytics, machine

learning, and reporting.

Data Governance: Implement data governance policies, ensuring compliance with data

security, privacy regulations (e.g., GDPR, CCPA), and internal standards.

Automation: Develop automated processes for data ingestion, transformation, and validation

to improve efficiency and reduce manual intervention.

Mentorship: Guide and mentor junior data engineers, fostering a culture of technical

excellence and continuous learning.

Troubleshooting: Diagnose and resolve complex data-related issues, ensuring high

availability and reliability of data systems.

Required Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science,

or a related field.

Experience: 5+ years of experience in data engineering or a related role, with a proven track

record of building scalable data pipelines and infrastructure.

Technical Skills:

Proficiency in programming languages such as Python, Java, or Scala.

Expertise in SQL and experience with NoSQL databases (e.g., MongoDB, Cassandra).

Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services

(e.g., Redshift, BigQuery, Snowflake).

Hands-on experience with ETL/ELT tools (e.g., Apache Airflow, Talend, Informatica) and

data integration frameworks.

Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka) and distributed

systems.

Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes) is a

plus.

Soft Skills:

Excellent problem-solving and analytical skills.

Strong communication and collaboration abilities.

Ability to work in a fast-paced, dynamic environment and manage multiple priorities.

Certifications (optional but preferred): Cloud certifications (e.g., AWS Certified Data Analytics,

Google Professional Data Engineer) or relevant data engineering certifications.

Preferred Qualifica

Experience with real-time data processing and streaming architectures.

Familiarity with machine learning pipelines and MLOps practices.

Knowledge of data visualization tools (e.g., Tableau, Power BI) and their integration with data

pipelines.

Experience in industries with high data complexity, such as finance, healthcare, or

e-commerce.

Work Environment

Location: Hybrid/Remote/On-site (depending on company policy).

Team: Collaborative, cross-functional team environment with data scientists, analysts, and

business stakeholders.

Hours: Full-time, with occasional on-call responsibilities for critical data systems.

Specific Responsibilities

• Minimum of 2 years Experience in Google Big Query and Google Cloud Platform.
• Design and develop the ETL framework using BigQuery
• Expertise in Big Query concepts like Nested Queries, Clustering, Partitioning, etc.
• Working Experience of Clickstream database, Google Analytics/ Adobe Analytics.
• Should be able to automate the data load from Big Query using APIs or scripting language.
• Good experience in Advanced SQL concepts.
• Good experience with Adobe launch Web, Mobile & e-commerce tag implementation.
• Identify complex fuzzy problems, break them down in smaller parts, and implement creative, data-driven solutions
• Responsible for defining, analyzing, and communicating key metrics and business trends to the management teams
• Identify opportunities to improve conversion & user experience through data. Influence product & feature roadmaps.
• Must have a passion for data quality and be constantly looking to improve the system. Drive data-driven decision making through the stakeholders & drive Change Management
• Understand requirements to translate business problems & technical problems into analytics problems.
• Effective storyboarding and presentation of the solution to the client and leadership.
• Client engagement & management
• Ability to interface effectively with multiple levels of management and functional disciplines.
• Assist in developing/coaching individuals technically as well as on soft skills during the project and as part of Client Project’s training program.

• 2 to 3 years of working experience in Google Big Query & Google Cloud Platform
• Relevant experience in Consumer Tech/CPG/Retail industries
• Bachelor’s in engineering, Computer Science, Math, Statistics or related discipline
• Strong problem solving and web analytical skills. Acute attention to detail.
• Experience in analyzing large, complex, multi-dimensional data sets.
• Experience in one or more roles in an online eCommerce or online support environment.

• Expertise in Google Big Query & Google Cloud Platform
• Experience in Advanced SQL, Scripting language (Python/R)
• Hands-on experience in BI tools (Tableau, Power BI)
• Working Experience & understanding of Adobe Analytics or Google Analytics
• Experience in creating and debugging website & app tracking (Omnibus, Dataslayer, GA debugger, etc.)
• Excellent analytical thinking, analysis, and problem-solving skills.
• Knowledge of other GCP services is a plus

We are seeking a dynamic and experienced Data Analytics and Data Science Trainer to deliver high-quality training sessions, mentor learners, and design engaging course content. The ideal candidate will have a strong foundation in statistics, programming, and data visualization tools, and should be passionate about teaching and guiding aspiring professionals.

Skills

• 5+ years of experience in a Data Engineer role
• Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases such as Cassandra.
• Experience with AWS cloud services: EC2, EMR, Athena
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
• Advanced SQL knowledge and experience working with relational databases, query authoring (SQL) as well as familiarity with unstructured datasets.
• Deep problem-solving skills to perform root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.