Tiger Analytics - Data Scientist - Timeseries Forecasting

Key Responsibilities:

- Forecasting & Modeling: Design, build, and deploy end-to-end forecasting models using statistical and machine learning techniques (e.g., ARIMA, Prophet, deep learning models, XGBoost).

- Big Data Processing: Utilize PySpark to process, clean, and engineer features from massive, distributed datasets.

- Data Analysis & Insights: Analyze large-scale datasets to identify trends, seasonality, and key drivers. Translate complex analytical findings into clear, actionable business insights for stakeholders.

- Pipeline & MLOps: Develop and maintain automated data and forecasting pipelines in a production environment. Implement best practices for model monitoring, version control (e.g., Git), and model performance tracking.

- Collaboration: Work closely with cross-functional teams, including data engineering, business intelligence, and business leaders, to understand requirements and ensure solutions align with business goals.

- Communication: Present complex methodologies and results to both technical and non-technical audiences, influencing strategic decisions with data-driven recommendations.

- Research & Development: Stay up-to-date on industry trends and advancements in forecasting methodologies and tools. Propose and implement innovative solutions to improve forecast accuracy and efficiency.

Qualifications:

- Experience: 5+ years of professional experience in a data science, forecasting, or advanced analytics role.

- Education: A bachelors or masters degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or a related discipline is required.

Technical Skills:

- Expertise in Python with a deep understanding of relevant libraries (e.g., scikit-learn, statsmodels, Prophet, PyTorch/TensorFlow).

- Proven proficiency with PySpark for large-scale data manipulation and model building.

- Strong knowledge of SQL for data extraction and analysis.

- Experience with Azure cloud platform & Data Bricks.

Soft Skills:

- Excellent problem-solving and analytical skills.

- Strong communication and storytelling abilities to convey complex ideas.

- Ability to work both independently and collaboratively in a fast-paced environment.

- A proactive and adaptable mindset, with a continuous learning approach.