Job Title: Data Science / ML Engineer (3+ years)
Location: Africa, Asia (Remote)
Work Experience (years): 3+ Years
Annual Compensation: $30 - $40k USD / month
Responsibilities
Release one new data product feature per quarter (including AI, statistics, LLM, or customer satisfaction-related features) with customer satisfaction over 80%.
Build and deploy at least three AI models per year (covering price prediction, grading systems, NLP, and portfolio development) that improve current performance by over 5%.
Ensure all strategies developed:
Beat the benchmark for at least 85% of the assets.
Allow for hourly updates with inference times under 15 minutes.
Are comprehensively documented.
Stay abreast of the latest developments in the crypto and AI fields by participating in at least two professional development activities per year.
Qualifications
A higher degree (M.S. or equivalent) in Data Science, Operations Research or AI
Proficiency in Python and Data Science libraries (numpy, pandas, matplotlib, sklearn, pytorch...), SQL and Databases, including Cloud databases
3 years of work experience in a Data Science/ML Engineer position
Expertise in Machine Learning and Deep Learning, including Forecasting models, Classification models, Regression models, Natural Language Processing, Price prediction, and Clustering Analysis.
Knowledge of Statistical Analysis and Data Analysis, including Hypothesis testing, ANOVA, and Visualization.
Important knowledge of Financial market, and if possible digital assets market, including Blockchain and Investing.
Bonus points for experience in Cloud computing, AWS, GCP, Azure, Apache Airflow, and deployment, as well as Big Data and Cloud Data Lake.
Skills: cloud computing, data analysis, classification models, crypto, ml, natural language processing, azure, forecasting, statistical analysis, nlp, forecasting models, python, anova, predictive analytics, gcp, price prediction, data science libraries, aws, sql, databases, data science, machine learning, hypothesis testing, apache airflow, visualization, blockchain knowledge, financial market knowledge, cloud databases, financial market, deep learning, investing, ai, clustering analysis, cloud data lake, blockchain, operations research, big data, regression models
