Job Location: Pune, Bangalore/Bengaluru
Company Profile
XpressBees a logistics company started in 2015 is amongst the fastest growing
companies of its sector. While we started off rather humbly in the space of
ecommerce B2C logistics, the last 5 years have seen us steadily progress towards
expanding our presence. Our vision to evolve into a strong full-service logistics
organization reflects itself in our new lines of business like 3PL, B2B Xpress and cross
border operations. Our strong domain expertise and constant focus on meaningful
innovation have helped us rapidly evolve as the most trusted logistics partner of
India. We have progressively carved our way towards best-in-class technology
platforms, an extensive 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 improve efficiencies for our clients.
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 ground-breaking AI/ML and Big Data 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 productization
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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