Job Description

Responsibilities

Collaborate and work across functional and multidisciplinary teams in a dynamic environment to develop an understanding of evolving/agile business needs. The Data Scientist role has work across the following four areas:

Exploratory data analysis:

• Conduct in-depth exploratory data analysis (EDA) to uncover hidden patterns, anomalies, and trends.

• Perform hypothesis testing and A/B testing to validate assumptions and measure the impact of data-driven decisions.

• Utilize statistical techniques and data visualization tools to communicate insights effectively to both technical and non-technical stakeholders.

Feature engineering and enrichment: 

• Create and refine relevant features from raw data to enhance model performance.

• Leverage large language models (LLMs) to generate novel features and augment existing datasets.

Modeling:

• Design, develop, and deploy advanced machine learning models (e.g., regression, classification, clustering) to address complex business problems.

• Continuously improve model performance by fine-tuning hyperparameters, exploring new algorithms, and incorporating feature engineering techniques.

• Collaborate with domain experts to understand business objectives and translate them into actionable data science solutions.

Data Operations and MLOps: 

• Develop and maintain efficient data pipelines to extract, transform, and load (ETL) data from various sources.

• Deploy and manage machine learning models in production environments using MLOps practices.

• Monitor model performance, retrain as needed, and implement automated retraining pipelines.

• Collaborate with software teams to ensure seamless integration of data science solutions into the broader technology stack.

Requirements and Qualifications:

• BS/MS in Science (Statistics, Computer Science, Econometrics, Data Science, Artificial Intelligence ).

• 1-5 years of experience with data science or computer science fields

• Experience with common data science toolkits, programming languages, visualization tools and SQL/NoSQL databases.

• Good applied statistical knowledge with emphasis in business and finance related statistical distributions, statistical testing, modeling, regression analysis, etc.

• Good foundation of computer science knowledge such as data structure, operating system

• Familiar or prone to adopt design thinking methods.

• Able to work under pressure and change, and balance among speed, reliability, interpretability.

• Experience with code versioning, code review and documentation.

• Effective communication skills

• Experience in building part-time projects which focuses on:

Machine Learning

• Understanding of machine learning algorithms such as k-NN, Naive Bayes, SVM, Decision trees.

• Experience using ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Experience with Google Cloud Platform products and services such as Vision API, Recommendations API, Cloud Natural Language.

GenAI

• Leverage the power of Generative AI to develop innovative solutions that push the boundaries of creativity and automation such as target labeling, building chatbot system, data enrichment, semantic search

• Experience in designing and implementing RAG systems, including knowledge base construction, information retrieval, and model fine-tuning.

Algorithm Engineering

• Strong ability to implement, improve, and deploy ML and Math models in Golang or Python.

• Conduct systems tests for security, performance, and availability.

• Develop and maintain the design and troubleshooting/error documentation.

• Create cost effective scalable systems and develop innovative algorithm solutions.

Operations Research

• Familiar with modeling problems as mathematical programming, constraint satisfaction, particle swarm optimization and other appropriate OR methodologies.

• Familiar with tools such as Cplex, Gurobi, Google OR-Tools.