Marketing Team Lead – B2B SaaS

• Experience working on Angular 2.X+
• Bootstrap/Angular Material development of web applications.
• Experience working on rest services-based architecture.
• Have used state management libraries, state Integration of Web APIs, and other 3rd party frameworks.
• Good understanding of HTML, CSS, ES6.
•  

Job Responsibilities

• Design, build and maintain software applications.
• Write well-designed, testable, efficient code by using best software development practices.

Company Profile
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.

• Hands-on Java Engineers, with experience building consumer-facing or enterprise applications using Java stack – Spring, Hibernate, MySQL
• Strong problem solving and analytical skills
• Strong understanding of Object-Oriented Programming concepts and Design patterns.

Do You Know? (Skills good to have)

• Exposure to building service-oriented distributed systems
• In building systems that process big data in a distributed environment, either in real-time streaming or offline batching.
• In messaging systems like Kafka, RabbitMQ, kinesis, etc.
• In real-time computation tools like Storm / Spark or Hadoop-based tools.
• In Data warehousing technologies like Redshift, BigQuery, etc.

 

 

URGENT HIRING # GHAR PE SHIKSHA
profile: Customer Sales Executive
Exp: 1 to 2 yrs
Sal. Based on your interview
Qualification: Min 12th Max Graduate
Age 18 to 28

Thorough understanding of React.js and its core principles and workflows such as Flux or Redux Experience with common front-end development tools such as Babel, Web pack, NPM, Yarn etc. Familiarity with server-side technologies like Node.js Familiarity with RESTful APIs Hands on skills with code versioning and configuration tools like Bitbucket Good understanding of browser compatibility and web standards Experience with Front-end performance optimization techniques Strong skills in communication, critical thinking, decision making, problem solving, and attention to detail Yrs Of Exp: 1-2 yrs dedicatedly in React.js Job Location: Pune Notice Period: Only Immediate Joinee

Role: Research Associate Position Description Research • Understanding the requirement of the client and creating a long list of the target organizations. • Mapping of various organizations and determining designations/ organization structures with accuracy. • Creatively network with people to get insight into organizational changes and developments to augment the database. • Track newspapers/ websites for developments in Financial Services (Private Equity Investments, Investment Banking Deals etc..) and related sectors. Delivery / Execution • Carry out widespread research to find suitable candidates for on-going positions. • Network extensively to gather meaningful data on the trends in the industry, challenges in the sector etc. in order to tap into the right area for sourcing. • Assist the team in managing Candidate – Client expectations. • Facilitate seamless Candidate – Client communication viz, interview scheduling. • Assist the team in handling the entire life cycle and thereby ensure smooth closure of the position. Report – Writing, Presentation & Database Management • Create mapping report basis the research done for various positions. • Report writing for on-going mandates – Long Lists, Candidate Evaluation, Comparative Analysis etc. • Update internal databank on real time basis. Desired Skills and Experience • Headhunting • Mapping • Cold Calling • Online Research • Professional Networking

Primary Skills : - B.Tech/MS/PhD degree in Computer Science, Computer Engineering or related technical discipline with 3-4 years of industry experience in Data Science. - Proven experience of working on unstructured and textual data. Deep understanding and expertise of NLP techniques (POS tagging, NER, Semantic role labelling etc). - Experience working with some of the supervised/unsupervised learning ML models such as linear/logistic regression, clustering, support vector machines (SVM), neural networks, Random Forest, CRF, Bayesian models etc. The ideal candidate will have a wide coverage of the different methods/models, and an in depth knowledge of some. - Strong coding experience in Python, R and Apache Spark. Python Skills are mandatory. - Experience with NoSQL databases, such as MongoDB, Cassandra, HBase etc. - Experience of working with Elastic search is a plus. - Experience of working on Microsoft Azure is a plus although not mandatory. - Basic knowledge of Linux and related scripting like Bash/shell script. Role Description (Roles & Responsibilities) : - Candidate will research, design and implement state-of-the-art ML systems using predictive modelling, deep learning, natural language processing and other ML techniques to help meeting business objectives. - Candidate will work closely with the product development/Engineering team to develop solutions for complex business problems or product features. - Handle Big Data scale for training and deploying ML/NLP based business modules/chatbots.