Role overview
**Position: ML Data Scientist Position**
**Location: Remote**
**Duration: 6-12 months of contract with possible extension**
**Role Summary:**
Ideal candidate will have strong
**Time-Series forecasting, sales forecasting, LGBM (LightGBM) and Darts library.**
**Qualifications:**
* Master’s plus degree in Computer Science, Statistics, Applied Mathematics, or a related field.
* 7+ years of experience in data science and machine learning, with a proven track record of delivering models to production.
* Proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
* Strong understanding of statistical modeling, machine learning algorithms, and experiment design.
* Solid experience with SQL and data manipulation tools (e.g., Pandas, Spark, or Dask).
* Experience deploying models using APIs (Flask, FastAPI), Docker, and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
* Hands-on experience with cloud platforms (AWS, GCP, or Azure) and model serving tools.
* Excellent problem-solving and communication skills; able to explain complex concepts clearly and effectively.
**Preferred:**
* Experience with time series forecasting, causal inference, recommendation systems, or NLP.
* Familiarity with data versioning and reproducibility tools (e.g., DVC, Weights & Biases).
* Exposure to feature stores, streaming data (e.g., Kafka), or real-time ML systems.
* Background in MLOps and experience building generalizable ML frameworks or platforms.
**Core Technical Skills:**
* ML Engineer Preferred: Ideally, the candidate should be an ML Engineer, though seasoned Data Scientists with relevant experience are suitable.
* Python & SQL: Strong coding and data manipulation skills.
* Time-Series Forecasting:Experience with LGBM (LightGBM) and Darts library.
* MLOps Expertise Preferred: Hands-on experience with Astronomer, Airflow, and DAG creation.
* Capable of building wrappers and scalable pipelines. This skill is highly valuable, but not a deal breaker.
* Cloud Platforms: Proficient in AWS, with exposure to GCP preferred.
* Debugging & Troubleshooting: Skilled in investigating and resolving issues in Python experiments and executions.
* GitHub Proficiency: Comfortable working in repositories with many contributors, managing branches, pull requests, and code reviews.
* Collaboration & Work Style
* Self-Starter: Able to work independently and proactively contribute ideas.
* Team-Oriented: Willing to support Roman and Calvin while offering directional guidance on model enhancements.
* Fast Learner: Quick to adapt to new tools, workflows, and business contexts to rapidly onboard into the project.