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Data Science
Python
Machine Learning (ML)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.
Description :
Experience : 3+ Years
Job Type : Full-time
Location : Sion, Mumbai (On-site)
Also Apply at : https://wohlig.keka.com/careers/jobdetails/122580
The Opportunity :
We are a global technology consultancy driving large-scale digital transformations for the Fortune 500. As a strategic partner to Google, we help enterprise clients navigate complex data landscapes migrating legacy systems to the cloud, optimizing costs, and turning raw data into executive-level insights.
We are seeking a Data Analyst who acts less like a technician and more like a Data Consultant. You will blend deep technical expertise in SQL and ETL with the soft skills required to tell compelling data stories to non-technical stakeholders.
What You Will Do :
- Strategic Cloud Data Architecture : Lead high-impact data migration projects. You will assess a client's legacy infrastructure and design the logic to move it to the cloud efficiently (focusing on scalability and security).
- Cost and Performance Optimization : Audit and optimize cloud data warehouses (e.g., BigQuery, Snowflake, Redshift). You will use logical reasoning to hunt down inefficiencies, optimize queries, and restructure data models to save clients significant operational costs.
- End-to-End Data Pipelines (ETL/ELT) : Build and maintain robust data pipelines. Whether its streaming or batch processing, you will ensure data flows seamlessly from source to dashboard using modern frameworks.
- Data Storytelling & Visualization : This is critical. You will build dashboards (Looker, Tableau, PowerBI) that don't just show numbers but answer business questions. You must be able to present these findings to C-suite clients with clarity and confidence.
- Advanced Analytics : Apply statistical rigor and logical deduction to solve unstructured business problems (e.g., Why is our user retention dropping?).
What We Are Looking For :
1. Core Competencies :
- Logical Reasoning : You possess a deductive mindset. You can break down ambiguous client problems into solvable technical components without needing hand-holding.
- Advanced SQL Mastery : You are fluent in complex SQL (Window functions, CTEs, stored procedures) and understand how to write query-efficient code for massive datasets.
- Communication Skills : You are an articulate storyteller who can bridge the gap between engineering jargon and business value.
2. Technical Experience :
- Cloud Proficiency : 3+ years of experience working within a major public cloud ecosystem (GCP, AWS, or Azure). Note : While we primarily use Google Cloud (BigQuery, Looker), we value strong architectural fundamentals over specific tool knowledge.
- Data Engineering & ETL : Experience with big data processing tools (e.g., Spark, Apache Beam, Databricks, or cloud-native equivalents).
- Visualization : Proven mastery of at least one enterprise BI tool (Looker, Tableau, PowerBI, Qlik).
Why Join us ?
- Cross-Cloud Exposure : While our current focus is GCP, your foundational skills will be challenged and expanded across various tech stacks.
- Client Impact : You aren't just writing code in a back room; you are the face of our data capability, directly advising clients on how to run their businesses better.
- Growth : We offer a structured career path with sponsorship for cloud certifications (Google Professional Data Engineer, etc.).
ROLES AND RESPONSIBILITIES:
You will be responsible for architecting, implementing, and optimizing Dremio-based data Lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.
- Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
- Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
- Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
- Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
- Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
- Support self-service analytics by enabling governed data products and semantic layers.
- Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
- Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.
IDEAL CANDIDATE:
- Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
- 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
- Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
- Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
- Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
- Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
- Excellent problem-solving, documentation, and stakeholder communication skills.
PREFERRED:
- Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
- Exposure to Snowflake, Databricks, or BigQuery environments.
- Experience in high-tech, manufacturing, or enterprise data modernization programs.
Data SciencePythonMachine Learning (ML)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.
Review Criteria
- Strong Dremio / Lakehouse Data Architect profile
- 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio
- Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems
- Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts
- Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)
- Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics
- Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices
- Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline
- Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies
Preferred
- Preferred (Nice-to-have) – Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) or data catalogs (Collibra, Alation, Purview); familiarity with Snowflake, Databricks, or BigQuery environments
Job Specific Criteria
- CV Attachment is mandatory
- How many years of experience you have with Dremio?
- Which is your preferred job location (Mumbai / Bengaluru / Hyderabad / Gurgaon)?
- Are you okay with 3 Days WFO?
- Virtual Interview requires video to be on, are you okay with it?
Role & Responsibilities
You will be responsible for architecting, implementing, and optimizing Dremio-based data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.
- Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
- Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
- Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
- Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
- Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
- Support self-service analytics by enabling governed data products and semantic layers.
- Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
- Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.
Ideal Candidate
- Bachelor’s or master’s in computer science, Information Systems, or related field.
- 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
- Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
- Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
- Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
- Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
- Excellent problem-solving, documentation, and stakeholder communication skills.
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