Data Scientist Openings
DATAMIMO offers a unique opportunity to develop both data analytics expertise and financial analysis domain knowledge. In this role, you will gain hands-on experience implementing the DuPont ratio analysis framework to assess fundamental stock performance. You will leverage proprietary algorithms inspired by Buffettology to identify high-potential long and short stock candidates from the S&P 500 index. Additionally, you will apply Modern Portfolio Theory (MPT) to construct optimized investment portfolios tailored to client needs.
What you will do:
Conduct data-driven stock analysis using quantitative financial models
Implement the DuPont analysis framework to evaluate company performance
Develop and apply proprietary algorithms for stock selection and ranking
Utilize Modern Portfolio Optimization techniques to construct diversified investment portfolios
Analyze and interpret financial datasets, identifying key insights to support investment decisions
Present findings through scientific reports, visualizations, and oral presentations
Collaborate with cross-functional teams to refine and enhance analytical methodologies
What we are looking for:
1+ years of experience in data analytics with a strong analytical mindset
Eagerness to master financial analysis and a keen interest in stock selection and portfolio optimization
Solid foundation in mathematics and statistics, particularly in data modeling and probability
Strong problem-solving skills and the ability to work in a collaborative team environment
Experience in writing reports, preparing presentations, and delivering insights effectively
Familiarity with data fetching techniques, such as web scraping or API integration (a plus)
Experience with web or app development (a plus)
If you’re passionate about applying data analytics to financial markets and eager to develop expertise in investment strategies, we’d love to hear from you!
DATAMIMO offers an exciting opportunity to develop your analytical skills and establish your career in data science by working on real-world real estate market data. In this role, you will gain hands-on experience in data collection, cleaning, and exploratory data analysis (EDA) to uncover hidden trends in different real estate markets. You will also apply machine learning techniques to train predictive models, estimating housing values and optimal asking/bidding prices. By comparing different model performances, you will refine your analytical approach and contribute to data-driven innovation in real estate analytics. If you’re passionate about leveraging data science to drive real-world impact, we invite you to embrace the challenge and join our team.
What you will do:
- Fetch, clean, and preprocess historical real estate data from various sources
- Conduct exploratory data analysis (EDA) to uncover trends and patterns in real estate markets
- Apply machine learning algorithms to build predictive models for home valuation and pricing strategies
- Compare and evaluate different models based on accuracy, interpretability, and robustness
- Develop interactive visualizations to communicate key insights effectively
- Collaborate with cross-functional teams to integrate predictive analytics into real-world applications
- Present findings through scientific reports and oral presentations to stakeholders
What we are looking for:
- 2+ years of experience in the intelligence community or relevant data analysis field
- Proficiency in at least one programming language (Python, R, or SQL preferred)
- Experience with data visualization techniques to present insights clearly and effectively
- Strong foundation in mathematics and statistics, particularly in data modeling and probability
- Ability to work collaboratively in a team environment, solving complex problems together
- Passion for data science and a strong desire to advance in the field
- Experience with machine learning, deep learning, or reinforcement learning (a plus)
If you’re excited about applying data science to real estate analytics and predictive modeling, we’d love to hear from you!
DATAMIMO offers a unique opportunity to enhance your expertise in data analytics and financial analysis using cutting-edge deep learning frameworks such as TensorFlow and Keras. In this role, you will gain hands-on experience in applying deep learning models for stock portfolio analysis, developing quantitative trading strategies, and backtesting investment approaches to assess their real-world performance. This is an exciting opportunity to work at the intersection of AI, finance, and data science, leveraging machine learning techniques to optimize stock selection and portfolio management.
What you will do:
- Develop and implement deep learning models for stock market analysis and prediction
- Design and test quantitative trading strategies, leveraging AI-driven insights
- Conduct backtesting to evaluate the effectiveness and robustness of trading models
- Perform financial data collection and preprocessing, ensuring high-quality datasets for model training
- Utilize TensorFlow and Keras to build, train, and optimize predictive models for investment decisions
- Analyze portfolio performance and risk metrics using advanced mathematical and statistical methods
- Present findings through scientific reports, visualizations, and oral presentations
- Collaborate with cross-functional teams to enhance deep learning models and financial analytics
What we are looking for:
- 3+ years of experience in data analytics, with a strong focus on machine learning and AI applications
- Proficiency in Python programming, particularly using Jupyter Notebook for data analysis and model development
- Expertise in TensorFlow and Keras, with hands-on experience in deep learning model implementation
- Strong understanding of financial analysis, including stock selection, portfolio construction, and risk assessment
- Solid foundation in mathematics and statistics, particularly in probability, optimization, and time series analysis
- Experience in data fetching from the Internet, including web scraping and API integration
- Ability to write clear, data-driven reports and deliver compelling scientific presentations
- Familiarity with PyTorch or other deep learning frameworks (a plus)
If you’re passionate about applying deep learning to financial markets and excited to develop AI-driven investment strategies, we’d love to hear from you!
DATAMIMO offers a unique opportunity to develop your analytical skills and establish a career in data science by working with real estate market data and advanced deep learning frameworks. In this role, you will gain hands-on experience in data collection, cleaning, and exploratory data analysis (EDA) to uncover trends in different real estate markets. You will apply machine learning techniques, with a focus on deep learning models using TensorFlow and Keras, to train predictive models for home valuation and pricing optimization. If you’re passionate about leveraging AI for real-world financial insights, this role will accelerate your career in data science.
What you will do:
- Fetch, clean, and preprocess historical real estate data from various online sources
- Perform exploratory data analysis (EDA) to identify patterns and trends in housing markets
- Develop deep learning models using TensorFlow and Keras to predict home values and optimize pricing strategies
- Train and fine-tune predictive models, comparing performance across different architectures
- Utilize statistical and mathematical techniques to improve model accuracy and interpretability
- Present findings through scientific reports, visualizations, and oral presentations
- Collaborate with cross-functional teams to integrate AI-driven real estate insights into business applications
What we are looking for:
- 3+ years of experience in the intelligence community or relevant data analysis field
- Proficiency in Python programming, particularly using Jupyter Notebook for data analysis and model development
- Expertise in TensorFlow and Keras, with hands-on experience in deep learning model implementation
- Strong understanding of mathematics and statistics, particularly in probability, optimization, and time series analysis
- Experience in data fetching from the Internet, including web scraping and API integration
- Ability to write clear, data-driven reports and deliver compelling scientific presentations
- Familiarity with PyTorch or other deep learning frameworks (a plus)