Qureos

FIND_THE_RIGHTJOB.

Applied Scientist

JOB_REQUIREMENTS

Hires in

Not specified

Employment Type

Not specified

Company Location

Not specified

Salary

Not specified

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

Job Types: Full-time, Permanent

Pay: ₹1,500,000.00 - ₹3,000,000.00 per year

Application Question(s):

  • Last Working Date?

Experience:

  • Total Work: 5 years (Preferred)
  • Hospitality: 3 years (Preferred)

Work Location: Remote

© 2025 Qureos. All rights reserved.