Data Scientist

JOB TITLE: Data Scientist (2–5 Years Experience)

About Crest.ai

Crest.ai is building the AI brain for supply chains — predicting demand, automating procurement, and optimizing distribution. Our integrated platform is already powering leading brands, helping them reduce stockouts, cut excess inventory, and drive efficiency across their supply chain. By combining machine learning, real-time analytics, and automation, we make supply meet demand — seamlessly and intelligently.

What You’ll Do

• Forecast the Future: Design, implement, and optimize machine learning models tailored to Time Series data, including ARIMA, LSTMs, and Transformers.

• Model the Trends: Work with regression models (linear, ridge, lasso, polynomial, etc.) to analyze and predict trends, behaviours, and business outcomes.

• Data Wrangling Wizardry: Clean, preprocess, and structure Time Series and tabular data for modelling and analysis.

• Experiment and Research: Explore advanced algorithms and hybrid approaches to combine Time Series models with regression techniques.

• Optimize for Scale: Develop scalable and production-ready solutions that integrate seamlessly into real-world supply-chain systems.

• Collaborate and Innovate: Work closely with cross-functional teams, including product managers, data engineers, and other researchers, to align technical solutions with business needs.

• Stay on the Cutting Edge: Keep up with the latest advancements in machine learning, Time Series analysis, and regression modelling to keep our solutions ahead of the curve.

What You’ll Bring

• Time Series Expertise: Deep understanding of Time Series analysis, including forecasting, feature engineering, and anomaly detection.

• Regression Know-How: Strong experience building and optimizing regression models such as linear, ridge, lasso, polynomial, and logistic regression.

• ML Mastery: Hands-on experience with ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, and XGBoost.

• Data Wizardry: Proficiency in Python, and experience working with libraries like Pandas, NumPy, and statsmodels.

• Visualization Ninja: Ability to visualize trends, forecasts, and patterns using tools like Matplotlib, Plotly, or Seaborn.

• Mathematical Rigor: Strong foundation in statistics, probability, and optimization.

• Cloud Savvy: Experience deploying ML models on platforms like AWS, GCP, or Azure.

• Bonus Points: Experience with hybrid models (e.g., combining Time Series with regression techniques), Reinforcement Learning, or real-time ML systems.

Note: This role requires mandatory experience in time series analysis and Python. Candidates should have experience working on any cloud platform. Exposure to machine learning models is important, along with hands-on experience in building end-to-end ML pipelines and model optimization.