Job Summary

We are searching for a Data Scientist to join our team of top-tier specialists, responsible for applying their expertise in machine learning, data mining, and information retrieval to design, prototype, and build next-generation analytics engines and services

Key Responsibilities

• Understanding business problems and designing smart data products.
• Developing complex models and algorithms to drive innovation within the organization.
• Conducting advanced statistical analysis to provide actionable insights, identify trends, and measure performance.
• Collaborating with data engineers to implement and deploy scalable solutions.
• Working closely with business teams to clarify ambiguous projects into concrete requirements.
• Designing and implementing time-series forecasting, anomaly detection, and demand-sensing solutions for business domains such as sales, marketing, pricing, supply chain, and logistics.
• Partnering with software and full-stack engineering teams to expose models through APIs, dashboards, and scalable user-facing applications that support day-to-day business decisions.

Skill Requirements

MS or PhD in Computer Science, Statistics, Mathematics, Artificial Intelligence, Physics, or a related technical discipline. At least 5 years of experience in a statistical and/or data science role. Expertise in Python (experience with languages like R, MATLAB, Scala is a plus). 2+ years of hands-on Python ML Production experience (not just POC), with proficiency in deploying models to real-world systems. 3+ years of professional experience as a Data Scientist, with a focus on applying machine learning models to business problems. Strong software engineering skills, including unit testing, object-oriented programming, and familiarity with best practices like PEP8 and tools like Black. Familiarity with Azure Machine Learning (1+ year of experience) and building cloud-based ML solutions. Experience in Bayesian modeling (1+ year) a

Other Requirements

• Experience in Bayesian modeling (1+ year) and the application of probabilistic models.
• Fluency in the NumFOCUS stack: pandas, scikit-learn, Matplotlib, and SciPy.
• Experience working with large datasets using tools like Spark and RDBMs.
• Experience with version control systems (e.g., Git) and knowledge of DevOps tools (Docker, Kubernetes, CI/CD pipelines).
• Proven ability to deploy models to production and plan products, considering the broader technical landscape.
• Professional attitude and service orientation.
• Team player with the ability to work autonomously on complex projects.
• Fluent English, as you will communicate primarily in English.
• Hands-on experience with time-series algorithms and forecasting methods such as ARIMA, SARIMA, SARIMAX, Exponential Smoothing, Prophet, VAR, and state-space models, along with modern machine learning and deep learning approaches such as XGBoost, LightGBM, LSTM, GRU, and Transformer-based forecasting.
• Strong understanding of time-series feature engineering, trend and seasonality decomposition, lag-based modeling, anomaly detection, model backtesting, and forecast evaluation using metrics such as MAE, RMSE, MAPE, sMAPE, and WAPE.
• Good full-stack engineering experience, including building and consuming REST APIs using FastAPI or Flask, integrating analytical services into microservice-based architectures, and collaborating on frontend applications built with frameworks such as React or Angular.
• Experience in delivering end-to-end data products with SQL/NoSQL databases, containerized deployments, cloud services, orchestration, observability, and secure production practices for business-facing analytical applications.
• Experience with Azure stack would be a plus.