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Python
Machine Learning (ML)Job Description: Applied Scientist
Location: Bangalore / Hybrid / Remote
Company: LodgIQ
Industry: Hospitality / SaaS / Machine Learning
About LodgIQ
Headquartered in New York, LodgIQ delivers a revolutionary B2B SaaS platform to the
travel industry. By leveraging machine learning and artificial intelligence, we enable precise
forecasting and optimized pricing for hotel revenue management. Backed by Highgate
Ventures and Trilantic Capital Partners, LodgIQ is a well-funded, high-growth startup with a
global presence.
About the Role
We are seeking a highly motivated Applied Scientist to join our Data Science team. This
individual will play a key role in enhancing and scaling our existing forecasting and pricing
systems and developing new capabilities that support our intelligent decision-making
platform.
We are looking for team members who: ● Are deeply curious and passionate about applying machine learning to real-world
problems. ● Demonstrate strong ownership and the ability to work independently. ● Excel in both technical execution and collaborative teamwork. ● Have a track record of shipping products in complex environments.
What You’ll Do ● Build, train, and deploy machine learning and operations research models for
forecasting, pricing, and inventory optimization. ● Work with large-scale, noisy, and temporally complex datasets. ● Collaborate cross-functionally with engineering and product teams to move models
from research to production. ● Generate interpretable and trusted outputs to support adoption of AI-driven rate
recommendations. ● Contribute to the development of an AI-first platform that redefines hospitality revenue
management.
Required Qualifications ● Bachelor's or Master’s degree or PhD in Computer Science or related field. ● 3-5 years of hands-on experience in a product-centric company, ideally with full model
lifecycle exposure.
Commented [1]: Leaving note here
Acceptable Degree types - Masters or PhD
Fields
Operations Research
Industrial/Systems Engineering
Computer Science
Applied Mathematics
● Demonstrated ability to apply machine learning and optimization techniques to solve
real-world business problems.
● Proficient in Python and machine learning libraries such as PyTorch, statsmodel,
LightGBM, scikit-learn, XGBoost
● Strong knowledge of Operations Research models (Stochastic optimization, dynamic
programming) and forecasting models (time-series and ML-based).
● Understanding of machine learning and deep learning foundations.
● Translate research into commercial solutions
● Strong written and verbal communication skills to explain complex technical concepts
clearly to cross-functional teams.
● Ability to work independently and manage projects end-to-end.
Preferred Experience
● Experience in revenue management, pricing systems, or demand forecasting,
particularly within the hotel and hospitality domain.
● Applied knowledge of reinforcement learning techniques (e.g., bandits, Q-learning,
model-based control).
● Familiarity with causal inference methods (e.g., DAGs, treatment effect estimation).
● Proven experience in collaborative product development environments, working closely
with engineering and product teams.
Why LodgIQ?
● Join a fast-growing, mission-driven company transforming the future of hospitality.
● Work on intellectually challenging problems at the intersection of machine learning,
decision science, and human behavior.
● Be part of a high-impact, collaborative team with the autonomy to drive initiatives from
ideation to production.
● Competitive salary and performance bonuses.
● For more information, visit https://www.lodgiq.com
Data ScienceMachine Learning (ML)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.
Data Science- 3-5yrs of practical DS experience working with varied data sets. Working with retail banking is preferred but not necessary.
- Need to be strong in concepts of statistical modelling – particularly looking for practical knowledge learnt from work experience (should be able to give "rule of thumb" answers)
- Strong problem solving skills and the ability to articulate really well.
- Ideally, the data scientist should have interfaced with data engineering and model deployment teams to bring models / solutions to "live" in production.
- Strong working knowledge of python ML stack is very important here.
- Willing to work on diverse range of tasks in building ML related capability on the Corridor Platform as well as client work.
- Someone with strong interest in data engineering aspect of ML is highly preferred, i.e. can play dual role of Data Scientist as well as someone who can code a module on our Corridor Platform writing robust code.
Structured ML techniques for candidates:
- GBM
- XgBoost
- Random Forest
- Neural Net
- Logistic Regression
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