AlgoDriven is looking for a rockstar data scientist to join our team at our automotive data company!
At AlgoDriven, we empower car dealers, banks and insurance companies with the data and tools to make data-driven decisions about pricing used cars. We’re a fast-growing, Silicon Valley Venture Capital backed startup, operating across the Middle East and Australia/Pacific. Were focused on disrupting the automotive industry and empowering the digital shift in Automotive.
The role requires working with the engineering team based in Dubai. The primary responsibility is enhancing and improving our existing data science models – to increase accuracy, improve their features and enhance explainability. You’ll work with large datasets around vehicles and pricing, to produce actionable results and insights for used car valuation.
We’re looking for fresh ideas and problem-solving, to help overcome obstacles!
MAIN ROLE RESPONSIBILITY
Bring experience, leadership and best practices to the Data Science team. Assist with problem-solving on improving the models and implementing new features.
Develop and optimise our vehicle price prediction algorithms and models.
Drive additional insights and understandings from our core algorithms and models.
Adapt our existing models and apply these learning as we scale into more markets.
Transform fuzzy/high-level requirements and objectives to solvable problem statements and requirements
Research and identify novel and practical solutions; define and communicate solution designs and approaches.
Research, design and implement descriptive, predictive and inferential models using artificial intelligence and machine learning techniques.
YOUR PROFILE & SKILLS
Previous experience (3+ years), in the data science field, preferably working on predicting values from time-series data sets.
Proven experience designing and developing artificial intelligence or machine learning applications at scale.
Strong academic knowledge and hands-on experience (3+ years) in data mining, applied statistics and machine learning.
Experience with mathematical and statistical data analysis techniques (such as regression, extrapolation, decision tree analysis, random walk, Monte-Carlo simulation, time series analysis, forecasting, statistical hypothesis testing and econometric models).
Advanced knowledge of SQL and Python.
Experience with languages and tools for data manipulation, analysis and plotting (R, R-shiny, R-markdown, Python, Pandas, Matplotlib, Bokeh, Jupyter).
Experience in performing A/B & multivariate testing to measure the impact of applied models.