XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.
● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.
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- You're proficient in GPT-3 based algorithms
- You have a passion for writing code as well as understanding and crafting the ways systems interact
- You believe in the benefits of agile processes and shipping code often
- You are pragmatic and work to coalesce requirements into reasonable solutions that provide value
Responsibilities
- Deploy well-tested, maintainable and scalable software solutions
- Take end-to-end ownership of the technology stack and product
- Collaborate with other engineers to architect scalable technical solutions
- Embrace and improve our standards and processes to reduce friction and unlock efficiency
Current Ecosystem :
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Game : Shiba Eternity on iOS and Android
As a Senior Engineer - Big Data Analytics, you will help the architectural design and development for Healthcare Platforms, Products, Services, and Tools to deliver the vision of the Company. You will significantly contribute to engineering, technology, and platform architecture. This will be done through innovation and collaboration with engineering teams and related business functions. This is a critical, highly visible role within the company that has the potential to drive significant business impact.
The scope of this role will include strong technical contribution in the development and delivery of Big Data Analytics Cloud Platform, Products and Services in collaboration with execution and strategic partners.
Responsibilities:
- Design & develop, operate, and drive scalable, resilient, and cloud native Big Data Analytics platform to address the business requirements
- Help drive technology transformation to achieve business transformation, through the creation of the Healthcare Analytics Data Cloud that will help Change establish a leadership position in healthcare data & analytics in the industry
- Help in successful implementation of Analytics as a Service
- Ensure Platforms and Services meet SLA requirements
- Be a significant contributor and partner in the development and execution of the Enterprise Technology Strategy
Qualifications:
- At least 2 years of experience software development for big data analytics, and cloud. At least 5 years of experience in software development
- Experience working with High Performance Distributed Computing Systems in public and private cloud environments
- Understands big data open-source eco-systems and its players. Contribution to open source is a strong plus
- Experience with Spark, Spark Streaming, Hadoop, AWS/Azure, NoSQL Databases, In-Memory caches, distributed computing, Kafka, OLAP stores, etc.
- Have successful track record of creating working Big Data stack that aligned with business needs, and delivered timely enterprise class products
- Experience with delivering and managing scale of Operating Environment
- Experience with Big Data/Micro Service based Systems, SaaS, PaaS, and Architectures
- Experience Developing Systems in Java, Python, Unix
- BSCS, BSEE or equivalent, MSCS preferred
- Responsible for setting up a scalable Data warehouse
- Building data pipeline mechanisms to integrate the data from various sources for all of Klub’s data.
- Setup data as a service to expose the needed data as part of APIs.
- Have a good understanding on how the finance data works.
- Standardize and optimize design thinking across the technology team.
- Collaborate with stakeholders across engineering teams to come up with short and long-term architecture decisions.
- Build robust data models that will help to support various reporting requirements for the business , ops and the leadership team.
- Participate in peer reviews , provide code/design comments.
- Own the problem and deliver to success.
Requirements:
- Overall 3+ years of industry experience
- Prior experience on Backend and Data Engineering systems
- Should have at least 1 + years of working experience in distributed systems
- Deep understanding on python tech stack with the libraries like Flask, scipy, numpy, pytest frameworks.
- Good understanding of Apache Airflow or similar orchestration tools.
- Good knowledge on data warehouse technologies like Apache Hive or similar. Good knowledge on Apache PySpark or similar.
- Good knowledge on how to build analytics services on the data for different reporting and BI needs.
- Good knowledge on data pipeline/ETL tools Hevo data or similar. Good knowledge on Trino / graphQL or similar query engine technologies.
- Deep understanding of concepts on Dimensional Data Models. Familiarity with RDBMS (MySQL/ PostgreSQL) , NoSQL (MongoDB/DynamoDB) databases & caching(redis or similar).
- Should be proficient in writing SQL queries.
- Good knowledge on kafka. Be able to write clean, maintainable code.
- Built a Data Warehouse from the scratch and set up a scalable data infrastructure.
- Prior experience in fintech would be a plus.
- Prior experience on data modelling.
Big Data Engineer/Data Engineer
What we are solving
Welcome to today’s business data world where:
• Unification of all customer data into one platform is a challenge
• Extraction is expensive
• Business users do not have the time/skill to write queries
• High dependency on tech team for written queries
These facts may look scary but there are solutions with real-time self-serve analytics:
• Fully automated data integration from any kind of a data source into a universal schema
• Analytics database that streamlines data indexing, query and analysis into a single platform.
• Start generating value from Day 1 through deep dives, root cause analysis and micro segmentation
At Propellor.ai, this is what we do.
• We help our clients reduce effort and increase effectiveness quickly
• By clearly defining the scope of Projects
• Using Dependable, scalable, future proof technology solution like Big Data Solutions and Cloud Platforms
• Engaging with Data Scientists and Data Engineers to provide End to End Solutions leading to industrialisation of Data Science Model Development and Deployment
What we have achieved so far
Since we started in 2016,
• We have worked across 9 countries with 25+ global brands and 75+ projects
• We have 50+ clients, 100+ Data Sources and 20TB+ data processed daily
Work culture at Propellor.ai
We are a small, remote team that believes in
• Working with a few, but only with highest quality team members who want to become the very best in their fields.
• With each member's belief and faith in what we are solving, we collectively see the Big Picture
• No hierarchy leads us to believe in reaching the decision maker without any hesitation so that our actions can have fruitful and aligned outcomes.
• Each one is a CEO of their domain.So, the criteria while making a choice is so our employees and clients can succeed together!
To read more about us click here:
https://bit.ly/3idXzs0
About the role
We are building an exceptional team of Data engineers who are passionate developers and wants to push the boundaries to solve complex business problems using the latest tech stack. As a Big Data Engineer, you will work with various Technology and Business teams to deliver our Data Engineering offerings to our clients across the globe.
Role Description
• The role would involve big data pre-processing & reporting workflows including collecting, parsing, managing, analysing, and visualizing large sets of data to turn information into business insights
• Develop the software and systems needed for end-to-end execution on large projects
• Work across all phases of SDLC, and use Software Engineering principles to build scalable solutions
• Build the knowledge base required to deliver increasingly complex technology projects
• The role would also involve testing various machine learning models on Big Data and deploying learned models for ongoing scoring and prediction.
Education & Experience
• B.Tech. or Equivalent degree in CS/CE/IT/ECE/EEE 3+ years of experience designing technological solutions to complex data problems, developing & testing modular, reusable, efficient and scalable code to implement those solutions.
Must have (hands-on) experience
• Python and SQL expertise
• Distributed computing frameworks (Hadoop Ecosystem & Spark components)
• Must be proficient in any Cloud computing platforms (AWS/Azure/GCP) • Experience in in any cloud platform would be preferred - GCP (Big Query/Bigtable, Pub sub, Data Flow, App engine )/ AWS/ Azure
• Linux environment, SQL and Shell scripting Desirable
• Statistical or machine learning DSL like R
• Distributed and low latency (streaming) application architecture
• Row store distributed DBMSs such as Cassandra, CouchDB, MongoDB, etc
. • Familiarity with API design
Hiring Process:
1. One phone screening round to gauge your interest and knowledge of fundamentals
2. An assignment to test your skills and ability to come up with solutions in a certain time
3. Interview 1 with our Data Engineer lead
4. Final Interview with our Data Engineer Lead and the Business Teams
Preferred Immediate Joiners
Work Timings:4:00PM to 11:30PM
Fulltime WFH
6+ Yrs in Data science
Strong Experience ML Regression, Classification, Anomaly detection, NLP, Deep learning, Predictive analytics, Predictive maintenance ,Python, Added advantage Data visualization
- Participate in full machine learning Lifecycle including data collection, cleaning, preprocessing to training models, and deploying them to Production.
- Discover data sources, get access to them, ingest them, clean them up, and make them “machine learning ready”.
- Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
- Partner with data scientists to understand and implement machine learning algorithms.
- Support A/B tests, gather data, perform analysis, draw conclusions on the impact of your models.
- Work cross-functionally with product managers, data scientists, and product engineers, and communicate results to peers and leaders.
- Mentor junior team members
Who we have in mind:
- Graduate in Computer Science or related field, or equivalent practical experience.
- 4+ years of experience in software engineering with 2+ years of direct experience in the machine learning field.
- Proficiency with SQL, Python, Spark, and basic libraries such as Scikit-learn, NumPy, Pandas.
- Familiarity with deep learning frameworks such as TensorFlow or Keras
- Experience with Computer Vision (OpenCV), NLP frameworks (NLTK, SpaCY, BERT).
- Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).
- Understand machine learning principles (training, validation, etc.)
- Strong hands-on knowledge of data query and data processing tools (i.e. SQL)
- Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code.
- Experience deploying highly scalable software supporting millions or more users
- Experience building applications on cloud (AWS or Azure)
- Experience working in scrum teams with Agile tools like JIRA
- Strong oral and written communication skills. Ability to explain complex concepts and technical material to non-technical users
- Expert software implementation and automated testing
- Promoting development standards, code reviews, mentoring, knowledge sharing
- Improving our Agile methodology maturity
- Product and feature design, scrum story writing
- Build, release, and deployment automation
- Product support & troubleshooting
Who we have in mind:
- Demonstrated experience as a Java
- Should have a deep understanding of Enterprise/Distributed Architecture patterns and should be able to demonstrate the relevant usage of the same
- Turn high-level project requirements into application-level architecture and collaborate with the team members to implement the solution
- Strong experience and knowledge in Spring boot framework and microservice architecture
- Experience in working with Apache Spark
- Solid demonstrated object-oriented software development experience with Java, SQL, Maven, relational/NoSQL databases and testing frameworks
- Strong working experience with developing RESTful services
- Should have experience working on Application frameworks such as Spring, Spring Boot, AOP
- Exposure to tools – Jira, Bamboo, Git, Confluence would be an added advantage
- Excellent grasp of the current technology landscape, trends and emerging technologies
Responsibilities for Data Scientist/ NLP Engineer
Work with customers to identify opportunities for leveraging their data to drive business
solutions.
• Develop custom data models and algorithms to apply to data sets.
• Basic data cleaning and annotation for any incoming raw data.
• Use predictive modeling to increase and optimize customer experiences, revenue
generation, ad targeting and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Deployment of ML model in production.
Qualifications for Junior Data Scientist/ NLP Engineer
• BS, MS in Computer Science, Engineering, or related discipline.
• 3+ Years of experience in Data Science/Machine Learning.
• Experience with programming language Python.
• Familiar with at least one database query language, such as SQL
• Knowledge of Text Classification & Clustering, Question Answering & Query Understanding,
Search Indexing & Fuzzy Matching.
• Excellent written and verbal communication skills for coordinating acrossteams.
• Willing to learn and master new technologies and techniques.
• Knowledge and experience in statistical and data mining techniques:
GLM/Regression, Random Forest, Boosting, Trees, text mining, NLP, etc.
• Experience with chatbots would be bonus but not required
SQL, Python, Numpy,Pandas,Knowledge of Hive and Data warehousing concept will be a plus point.
JD
- Strong analytical skills with the ability to collect, organise, analyse and interpret trends or patterns in complex data sets and provide reports & visualisations.
- Work with management to prioritise business KPIs and information needs Locate and define new process improvement opportunities.
- Technical expertise with data models, database design and development, data mining and segmentation techniques
- Proven success in a collaborative, team-oriented environment
- Working experience with geospatial data will be a plus.