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Data Scientist | Python | SQL
Ozkan Batumoglu, MSc
,
London, United Kingdom
Experience
Other titles
Skills
I'm offering
Business-minded professional with hands-on experience in financial services sector. Proven success providing valuable insights via data analytics and advanced data-driven methods. Track record of delivering data-driven, action-oriented solutions to challenging business problems and building statistical models to improve customer experience and optimise processes. Relied on as a key advisor in driving global, multibillion-dollar growth; gains in customer loyalty and record-setting profit improvements. Able to furnish executive leadership team with reports and recommendations enabling effective strategic planning across all business units, distribution channels, and product lines. Bi-lingual with operational command over English and Turkish.
Markets
United States
(Remote
only)
United Kingdom
France
(Remote
only)
Germany
(Remote
only)
Links for more
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Language
English
Fluently
Ready for
Larger project
Ongoing relation / part-time
Full time contractor
Available
My experience
2019 - ?
freelance
Freelance Data Scientist
Tech Stack.
- Built a data infrastructure for an e-commerce company in the US where all data is stored and visualized automatically. Wrote necessary BigQuery queries to flatten the data and connected necessary API's to make sure data pipeline is robust. After delivery, all raw data and generated insights could be monitored in the management dashboard.
-- Tech Stack: Shopify, Stitch, BigQuery, Data Studio, GCP
- Built a model for energy sector company to minimize MAPE. Data contained some categorical and timeseries variables. Therefore, applied common appropriate methods such as one-hot-encoding, normalization and trend detection. After generating some features, performance of the model was boosted.
-- Libraries: Python, Jupyter, SciPy, Pandas, Scikit-learn, NumPy, Matplotlib, Git, XGBOOST, CatBoost, LightGBM, SVM, statmodels
- Provided daily in-house training to companies covering general ML topics. The purpose of these trainings was to build a bridge between project managers, developers, business analysts and data scientists by providing common understanding how to build ML models. Some of these companies also requested coding-oriented training.
-- Covered topics: Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Data Preprocess
-- Tech Stack: Shopify, Stitch, BigQuery, Data Studio, GCP
- Built a model for energy sector company to minimize MAPE. Data contained some categorical and timeseries variables. Therefore, applied common appropriate methods such as one-hot-encoding, normalization and trend detection. After generating some features, performance of the model was boosted.
-- Libraries: Python, Jupyter, SciPy, Pandas, Scikit-learn, NumPy, Matplotlib, Git, XGBOOST, CatBoost, LightGBM, SVM, statmodels
- Provided daily in-house training to companies covering general ML topics. The purpose of these trainings was to build a bridge between project managers, developers, business analysts and data scientists by providing common understanding how to build ML models. Some of these companies also requested coding-oriented training.
-- Covered topics: Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Data Preprocess
Python, Git, API, Training, Shopify, E-commerce, SoMe, Management, Infrastructure, Energy
2019 - 2019
temp
Part Time Data Scientist
Sor'un.
• Built an NLP model based on fasttext technology for a multinational retailer company. This was a classification problem on customer text data with an accuracy score of 91%. The model was integrated with front-end and became a chatbot which later deployed on the production server.
-- Tech Stack: Python, Visual Studio Code, Fasttext, Pandas, Linux, Docker, AWS, REST API
• Increased closing rate of a chatbot in retail sector by 15% through preparation of an analytics report. The conversations which lead to real agent are filtered and misclassifications are labelled. Model dictionary is also expanded by considering common typos in text data.
-- Libraries: Python, Jupyter, Pandas, Numpy, Matplotlib, scikit-learn, wordcloud
-- Tech Stack: Python, Visual Studio Code, Fasttext, Pandas, Linux, Docker, AWS, REST API
• Increased closing rate of a chatbot in retail sector by 15% through preparation of an analytics report. The conversations which lead to real agent are filtered and misclassifications are labelled. Model dictionary is also expanded by considering common typos in text data.
-- Libraries: Python, Jupyter, Pandas, Numpy, Matplotlib, scikit-learn, wordcloud
Visual Studio, Agent, Production, Chatbot, Server, Visual studio code, NLP, Technology, Analytics, Python, REST, Retail, REST API, AWS, Docker, API, Linux
2017 - 2019
job
Lead Data Scientist
Subsidiary of OakNorth Bank UK.
• Managed property valuation project where user enters postcode and house type in UK and gets an estimated value. After gathering latest sales in the area, the model makes prediction by considering sales time and area value.
-- Tech Stack: Python, Visual Studio, Pandas, JIRA, Numpy, JS, Git, Docker, AWS, GUNICORN, REST API, Linux
• Managed analytics tool project where analysts can upload their excel and use advance statistical tools by drag and drop methodology. Analysts were able to perform clustering, time series analysis, anomaly detection and some advanced statistical analysis on the tool which was deployed in AWS.
-- Tech Stack: Python, Jupyter, JIRA, Docker, Git, AWS, GUNICORN, REST API, Linux
• Prepared an analytical report for a retailer customer where user data was anonymized. Data was aggregated based on branches and schedule. Suggested some changes on table distribution and scheduling which likely will increase sales and presented the report in C level meeting.
-- Tech Stack: Python, Jupyter, Pandas, NumPy, Scikit-learn, SciPy, SQL
• Worked with business analysts to understand credit lending process. Generated a roadmap to automate some of these processes in order to decrease cost and duration of generating credit report.
• Managed NLP project on 10K of public US stocks. Provided categorization and sentiment analysis on annual reports. Tool summarized the annual report and generated insights for analysts which shorten the analysis of the report 4 times.
-- Tech Stack: Python, SpaCy, Jupyter, Pandas, Scikit-learn, JS, Docker, Git, Linux
-- Tech Stack: Python, Visual Studio, Pandas, JIRA, Numpy, JS, Git, Docker, AWS, GUNICORN, REST API, Linux
• Managed analytics tool project where analysts can upload their excel and use advance statistical tools by drag and drop methodology. Analysts were able to perform clustering, time series analysis, anomaly detection and some advanced statistical analysis on the tool which was deployed in AWS.
-- Tech Stack: Python, Jupyter, JIRA, Docker, Git, AWS, GUNICORN, REST API, Linux
• Prepared an analytical report for a retailer customer where user data was anonymized. Data was aggregated based on branches and schedule. Suggested some changes on table distribution and scheduling which likely will increase sales and presented the report in C level meeting.
-- Tech Stack: Python, Jupyter, Pandas, NumPy, Scikit-learn, SciPy, SQL
• Worked with business analysts to understand credit lending process. Generated a roadmap to automate some of these processes in order to decrease cost and duration of generating credit report.
• Managed NLP project on 10K of public US stocks. Provided categorization and sentiment analysis on annual reports. Tool summarized the annual report and generated insights for analysts which shorten the analysis of the report 4 times.
-- Tech Stack: Python, SpaCy, Jupyter, Pandas, Scikit-learn, JS, Docker, Git, Linux
Jira, Processes, NLP, Sales, Analytics, Visual Studio, SoMe, C, REST, Sql, REST API, AWS, Docker, API, Linux, Git, Excel, Python
2014 - 2017
job
Senior Quantitative Analyst
Science Wave Capital.
• Built a ETL tool based on Bloomberg API to store necessary market and fundamental data which will be used to run algorithms. Generated appropriate tables on MySQL to feed data accordingly.
-- Tech Stack: C#, SQL, Bloomberg API
• Generated a pair algorithm where fundamental data is also used. The model was using analysts estimates, latest net income and stock price. This algorithm contributed to portfolio during my employment.
-- Tech Stack: C#, SQL, Wealth-Lab
• Built a momentum algorithm where small stocks are monitored. Stocks were grouped based on their business models and like hood of having a trend. This algorithm contributed to portfolio during my employment.
-- Tech Stack: C#, SQL, Wealth-Lab
• Applied a whitepaper about factor trading on python which turned out profitable and initiated a big strategy. This strategy was deployed in 15 European countries covering approximately 10000 stocks.
-- Teck Stack: Python, Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, ClariFI, Axiom, Excel
• Built models to predict future stock returns (hourly, daily, weekly, monthly) in the market. Factor trading white papers were used to generated ideas.
-- Tech Stack: Python, ClariFI, SQL, XGBOOST, CatBoost, LightGBM, Neural Networks, Axiom
• Generated market-based features which will be used in algorithms by ClariFI. These features had meaningful information coefficient. Tested these features in US, Europe and Turkish stock market.
• Implemented a VBA code to feed execution platform in the morning. The code reshaped output of back testing platform to the requirements of execution platform. After implementation 30 minutes saved daily.
-- Tech Stack: C#, SQL, Bloomberg API
• Generated a pair algorithm where fundamental data is also used. The model was using analysts estimates, latest net income and stock price. This algorithm contributed to portfolio during my employment.
-- Tech Stack: C#, SQL, Wealth-Lab
• Built a momentum algorithm where small stocks are monitored. Stocks were grouped based on their business models and like hood of having a trend. This algorithm contributed to portfolio during my employment.
-- Tech Stack: C#, SQL, Wealth-Lab
• Applied a whitepaper about factor trading on python which turned out profitable and initiated a big strategy. This strategy was deployed in 15 European countries covering approximately 10000 stocks.
-- Teck Stack: Python, Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, ClariFI, Axiom, Excel
• Built models to predict future stock returns (hourly, daily, weekly, monthly) in the market. Factor trading white papers were used to generated ideas.
-- Tech Stack: Python, ClariFI, SQL, XGBOOST, CatBoost, LightGBM, Neural Networks, Axiom
• Generated market-based features which will be used in algorithms by ClariFI. These features had meaningful information coefficient. Tested these features in US, Europe and Turkish stock market.
• Implemented a VBA code to feed execution platform in the morning. The code reshaped output of back testing platform to the requirements of execution platform. After implementation 30 minutes saved daily.
Mysql, Sql, Python, Excel, API, C, ETL, VBA, Algorithms, Net, Analyst, Implementation, Testing, Neural networks, Whitepaper
2010 - 2014
job
Statistical Arbitrage Trader
Is Investment.
Co-ordinated successful development and implementation of trading and momentum strategies. Traded Turkish derivatives and equities in proprietary trading desk. Employed effective strategies relevant to statistical/index arbitrage and NAV/pair trading.
• Accomplished all assigned annual targets within eight months in 2013.
• Constantly improved trading limits several times by proposing new statistical trading methods.
◦ Strategies: Pair Trading, NAV Trading, Calendar Spread, Index Arbitrage
• Streamlined the entire IPO process of ETF with legal and accounting teams
• Managed market making process of ETF in Turkish market with best spread and generated revenue and reputation for the company.
• Accomplished all assigned annual targets within eight months in 2013.
• Constantly improved trading limits several times by proposing new statistical trading methods.
◦ Strategies: Pair Trading, NAV Trading, Calendar Spread, Index Arbitrage
• Streamlined the entire IPO process of ETF with legal and accounting teams
• Managed market making process of ETF in Turkish market with best spread and generated revenue and reputation for the company.
Implementation, Development
2010 - 2010
internship
Trainee - Equity Derivatives
Deutsche Securities.
2009 - 2010
temp
Part Time Analyst
Turkey Advantage Fund.
Analyst
2009 - 2009
internship
Intern - Equity Research
EFG Istanbul Securities.
Research, Internal
2009 - 2009
job
Teaching Assistant - Macroeconomy
Koc University.
Teaching
2008 - 2008
internship
Intern - Research
YapiKredi Asset Management.
Research, Internal
My education
?
-
2017
Sabanci University
Masters, Applied Statistics
Masters, Applied Statistics
?
-
2010
Koc University
N/a, Industrial Engineering
N/a, Industrial Engineering
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