loanDNA
Company:
https://www.linkedin.com/company/loandna
Seniority: Mid-Senior level
Industries: Financial Services
Job details:
Experience – 5 to 10 years:
About the Role
We are looking for an experienced Data Scientist to join our team and help build the next generation of data-driven solutions for the mortgage and real estate industry.
In this role, you will work at the intersection of data science, machine learning, cloud technology, and large-scale data engineering. You will have the opportunity to take ownership of complex analytical problems, develop production-grade machine learning solutions, and work with large volumes of mortgage, property, valuation, market, and transactional data.
You will work closely with Data Engineering, Product, Technology, Analytics, and Business teams to turn complex business problems into scalable data science solutions.
Mortgage domain experience is preferred but not required. We are equally interested in candidates with strong data science fundamentals who are excited to learn the mortgage and real estate domain.
Who You Are
You are a hands-on Data Scientist who enjoys solving complex problems with data. You are comfortable working across the complete machine learning lifecycle — from understanding raw data and identifying patterns to feature engineering, model development, validation, deployment, and production monitoring. You are someone who can move between experimentation and production. You are equally comfortable writing Python to build a model, SQL to analyse hundreds of millions of records, and collaborating with engineers to operationalise a solution. You enjoy understanding the business problem behind the model and are able to communicate technical findings clearly to both technical and non-technical stakeholder
s. Most importantly, you are curious, analytical, and comfortable working in an environment where you are expected to explore, experiment, challenge assumptions, and continuously improve existing solutions.
What You’ll Do
As a Data Scientist, you will:
- Design, develop, and productionise machine learning and statistical models.
- Work with large-scale structured and unstructured datasets.
- Perform exploratory data analysis to identify patterns, trends, anomalies, and opportunities.
- Develop advanced feature engineering and data transformation pipelines.
- Build predictive models using techniques such as regression, tree-based models, gradient boosting, ensemble modelling, clustering, ranking, and deep learning where appropriate.
- Develop end-to-end ML pipelines covering data preparation, training, validation, deployment, scoring, and monitoring.
- Conduct model backtesting, benchmarking, hyperparameter optimisation, and performance analysis.
- Develop model explainability and interpretability using techniques such as SHAP and feature importance.
- Monitor model performance, data drift, feature drift, stability, coverage, and production accuracy.
- Develop scalable batch and real-time prediction pipelines.
- Build reusable analytical frameworks rather than one-off solutions.
- Design automated data-quality and model-quality checks.
- Investigate production issues and perform root-cause analysis.
- Identify opportunities to improve existing models, data pipelines, and analytical methodologies.
- Work closely with Data Engineers and Software Engineers to move models from experimentation into production.
- Translate business problems into measurable data science objectives.
- Present model results, insights, recommendations, and trade-offs to business stakeholders and leadership.
Technology You’ll Work With
Our environment includes modern cloud, data, and machine learning technologies.
You may work with:
Data & Cloud Platforms
- Snowflake
- Databricks
- AWS
- Microsoft Azure
- Google Cloud Platform
Programming & Data
- Python
- SQL
- PySpark
- Pandas
- NumPy
Machine Learning
- Scikit-learn
- XGBoost
- LightGBM
- CatBoost
- MLflow
- SHAP
- TensorFlow / PyTorch
Engineering & MLOps
- Git
- Docker
- Kubernetes
- REST APIs
- FastAPI
- CI/CD
- Model registries
- Automated ML pipelines
- Cloud-based model deployment and monitoring
You do not need experience with every technology listed above. We value strong fundamentals and the ability to learn new technologies quickly.
What We’re Looking For
Required
- Bachelor's or master’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, Economics, or another quantitative discipline.
- Strong professional experience in Data Science, Machine Learning, Advanced Analytics, or a related field.
- Strong proficiency in Python and SQL.
- Strong understanding of machine learning and statistical modelling.
- Experience developing predictive models using real-world datasets.
- Experience with feature engineering, model evaluation, and model optimisation.
- Experience working with large and complex datasets.
- Strong understanding of model validation and performance metrics.
- Ability to translate business requirements into analytical and machine learning solutions.
- Strong problem-solving and analytical skills.
- Ability to communicate complex technical concepts to different audiences.
What You’ll Get
This role provides the opportunity to:
- Work on complex, real-world machine learning problems with measurable business impact.
- Build models using large-scale mortgage and real estate datasets.
- Work with modern cloud and data platforms such as Snowflake and Databricks.
- Own solutions across the complete machine learning lifecycle.
- Build production systems rather than proof-of-concept models alone.
- Experiment with traditional machine learning, advanced analytics, and Generative AI.
Click on Apply to know more.
