About Quantela
We are a technology company that offers outcomes business models. We empower our customers with the right digital infrastructure to deliver greater economic, social, and environmental outcomes for their constituents.
When the company was founded in 2015, we specialized in smart cities technology alone. Today, working with cities and towns, utilities, and public venues, our team of 280+ experts offers a vast array of outcomes business models through technologies like digital advertising, smart lighting, smart traffic, and digitized citizen services.
We pride ourselves on our agility, innovation, and passion to use technology for a higher purpose. Unlike other technology companies, we tailor our offerings (what we can digitize) and the business model (how we partner with our customers to deliver that digitization) to drive measurable impact where our customers need it most.
Over the last several months alone, we have served customers to deliver outcomes like increased medical response times to save lives, reduced traffic congestion to keep cities moving, and created new revenue streams to tackle societal issues like homelessness.
We are headquartered in Billerica, Massachusetts in the United States with offices across Europe and Asia.
The company has been recognized with the World Economic Forum’s ‘Technology Pioneers’ award in 2019 and CRN’s IoT Innovation Award in 2020.
For the latest news and updates, please visit us at
www.quantela.com
Overview of the Role
We’re looking for a Data Science Engineer who knows how to turn complex data problems into real, scalable solutions. Someone who can balance hands-on model development with guiding a small team and shaping technical direction. You’ll work across machine learning, statistical modelling, and data engineering to build production-ready systems that deliver clear business impact.
Roles and Responsibilities
- Design, build, and deploy advanced machine learning and statistical models for forecasting, prediction, recommendation, and anomaly detection.
- Develop and optimize time series forecasting models for IoT sensor data using both traditional (ARIMA, SARIMA) and deep learning approaches (RNN, LSTM, GRU).
- Lead the end-to-end lifecycle of data science projects, from exploration and experimentation to productionization and performance monitoring.
- Build pipelines and workflows that support scalable model training and inference in production environments.
- Work with NLP, computer vision, and multimodal AI techniques to create practical, customer-facing solutions.
- Implement LLM/SLM-based applications using LangChain, LangGraph, and HuggingFace.
- Collaborate with data engineering and product teams to ensure smooth integration of models with existing systems.
- Provide technical leadership and mentorship to a team of 5–6 data science engineers.
- Join customer calls to present solutions, gather requirements, and ensure alignment with business needs.
Desired Skills and Background
- Bachelor’s or Master’s degree in Computer Science or a related field, plus certification in AI/ML.
- 4–5 years of industry experience in data science and applied machine learning.
- Strong Python development skills with hands-on use of key ML frameworks and libraries.
- Solid grounding in data mining, pattern analysis, and anomaly detection techniques.
- Proven experience building and deploying time series models for IoT data.
- Background in training ML models for predictive and prescriptive analytics using production datasets.
- Practical experience in NLP and computer vision projects.
- Familiarity with LLM/SLM tools, including LangChain, LangGraph, and HuggingFace.
- Exposure to cloud platforms and modern data engineering tools.
- Comfortable managing a small technical team and interacting directly with customers.