Machine Learning (ML)
Data ScienceXressBees – 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.
Subodh Popalwar
Software Engineer, Memorres
About xpressbees
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Data Science
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𝐖𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐲𝐨𝐮 𝐰𝐨𝐫𝐤 𝐨𝐧?
𝟏) Problem formulation and solution designing of ML/NLP applications across complex well-defined as well as open-ended healthcare problems.
2) Cutting-edge machine learning, data mining, and statistical techniques to analyse and utilise large-scale structured and unstructured clinical data.
3) End-to-end development of company proprietary AI engines - data collection, cleaning, data modelling, model training / testing, monitoring, and deployment.
4) Research and innovate novel ML algorithms and their applications suited to the problem at hand.
𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐰𝐞 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐟𝐨𝐫?
𝟏) Deeper understanding of business objectives and ability to formulate the problem as a Data Science problem.
𝟐) Solid expertise in knowledge graphs, graph neural nets, clustering, classification.
𝟑) Strong understanding of data normalization techniques, SVM, Random forest, data visualization techniques.
𝟒) Expertise in RNN, LSTM, and other neural network architectures.
𝟓) DL frameworks: Tensorflow, Pytorch, Keras
𝟔) High proficiency with standard database skills (e.g., SQL, MongoDB, Graph DB), data preparation, cleaning, and wrangling/munging.
𝟕) Comfortable with web scraping, extracting, manipulating, and analyzing complex, high-volume, high-dimensionality data from varying sources.
𝟖) Experience with deploying ML models on cloud platforms like AWS or Azure.
9) Familiarity with version control with GIT, BitBucket, SVN, or similar.
𝐖𝐡𝐲 𝐜𝐡𝐨𝐨𝐬𝐞 𝐮𝐬?
𝟏) We offer Competitive remuneration.
𝟐) We give opportunities to work on exciting and cutting-edge machine learning problems so you contribute towards transforming the healthcare industry.
𝟑) We offer flexibility to choose your tools, methods, and ways to collaborate.
𝟒) We always value and believe in new ideas and encourage creative thinking.
𝟓) We offer open culture where you will work closely with the founding team and have the chance to influence the product design and execution.
𝟔) And, of course, the thrill of being part of an early-stage startup, launching a product, and seeing it in the hands of the users.
Machine Learning (ML)Data ScienceJob Description
Lead Machine Learning (ML)/
NLP Engineer
5 + years of experience
About Contify
Contify is an AI-enabled Market and Competitive Intelligence (MCI)
software to help professionals make informed decisions. Its B2B SaaS
platform helps leading organizations such as Ericsson, EY, Wipro,
Deloitte, L&T, BCG, MetLife, etc. track information on their competitors,
customers, industries, and topics of interest by continuously monitoring
over 500,000+ sources on a real-time basis. Contify is rapidly growing
with 185+ people across two offices in India. Contify is the winner of
Frost and Sullivan’s Product Innovation Award for Market and
Competitive Intelligence Platforms.
The role
We are looking for a hardworking, aspirational, and innovative
engineering person for the Lead ML/ NLP Engineer position. You’ll build
Contify’s ML and NLP capabilities and help us extract value from
unstructured data. Using advanced NLP, ML, and text analytics, you will
develop applications that will extract business insights by analyzing a
large amount of unstructured text information, identifying patterns, and
by connecting the events.
Responsibilities:
You will be responsible for all the processes from data collection, and
pre-processing, to training models and deploying them to production.
➔ Understand the business objectives; design and deploy scalable
ML models/ NLP applications to meet those objectives
➔ Use of NLP techniques for text representation, semantic analysis,
information extraction, to meet the business objectives in an
efficient manner along with metrics to measure progress
➔ Extend existing ML libraries and frameworks and use effective text
representations to transform natural language into useful features
➔ Defining and supervising the data collection process, verifying data
quality, and employing data augmentation techniques
➔ Defining the preprocessing or feature engineering to be done on a
given dataset
➔ Analyze the errors of the model and design strategies to overcome
them
➔ Research and implement the right algorithms and tools for ML/
NLP tasks
➔ Collaborate with engineering and product development teams
➔ Represent Contify in external ML industry events and publish
thought leadership articles
Desired Skills and Experience
To succeed in this role, you should possess outstanding skills in
statistical analysis, machine learning methods, and text representation
techniques.
➔ Deep understanding of text representation techniques (such as n-
grams, bag of words, sentiment analysis, etc), statistics and
classification algorithms
➔ Hand on experience in feature extraction techniques for text
classification and topic mining
➔ Knowledge of text analytics with a strong understanding of NLP
algorithms and models (GLMs, SVM, PCA, NB, Clustering, DTs)
and their underlying computational and probabilistic statistics
◆ Word Embedding like Tfidf, Word2Vec, GLove, FastText, etc.
◆ Language models like Bert, GPT, RoBERTa, XLNet
◆ Neural networks like RNN, GRU, LSTM, Bi-LSTM
◆ Classification algorithms like LinearSVC, SVM, LR
◆ XGB, MultinomialNB, etc.
◆ Other Algos- PCA, Clustering methods, etc
➔ Excellent knowledge and demonstrable experience in using NLP
packages such as NLTK, Word2Vec, SpaCy, Gensim, Standford
CoreNLP, TensorFlow/ PyTorch.
➔ Experience in setting up supervised & unsupervised learning
models including data cleaning, data analytics, feature creation,
model selection & ensemble methods, performance metrics &
visualization
➔ Evaluation Metrics- Root Mean Squared Error, Confusion Matrix, F
Score, AUC – ROC, etc
➔ Understanding of knowledge graph will be a plus
Qualifications
➔ Education: Bachelors or Masters in Computer Science,
Mathematics, Computational Linguistics or similar field
➔ At least 4 years' experience building Machine Learning & NLP
solutions over open-source platforms such as SciKit-Learn,
Tensorflow, SparkML, etc
➔ At least 2 years' experience in designing and developing
enterprise-scale NLP solutions in one or more of: Named Entity
Recognition, Document Classification, Feature Extraction, Triplet
Extraction, Clustering, Summarization, Topic Modelling, Dialog
Systems, Sentiment Analysis
➔ Self-starter who can see the big picture, and prioritize your work to
make the largest impact on the business’ and customer’s vision
and requirements
➔ Being a committer or a contributor to an open-source project is a
plus
Note
Contify is a people-oriented company. Emotional intelligence, therefore,
is a must. You should enjoy working in a team environment, supporting
your teammates in pursuit of our common goals, and working with your
colleagues to drive customer value. You strive to not only improve
yourself, but also those around you.
To be considered as a candidate for a Senior Data Engineer position, a person must have a proven track record of architecting data solutions on current and advanced technical platforms. They must have leadership abilities to lead a team providing data centric solutions with best practices and modern technologies in mind. They look to build collaborative relationships across all levels of the business and the IT organization. They possess analytic and problem-solving skills and have the ability to research and provide appropriate guidance for synthesizing complex information and extract business value. Have the intellectual curiosity and ability to deliver solutions with creativity and quality. Effectively work with business and customers to obtain business value for the requested work. Able to communicate technical results to both technical and non-technical users using effective story telling techniques and visualizations. Demonstrated ability to perform high quality work with innovation both independently and collaboratively.
Machine Learning (ML)Data Science
Data SciencePythonMachine Learning (ML)Deep Learning
Follow CutshortSubodh Popalwar
Software Engineer, Memorres