Sr ML Engineer and Data Scientist

**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.