Jānis Veits

Data scientist

Riga, Latvia

About

Experienced Data Analytics Consultant interested in data science. Skilled in Excel, Python, R, SQL and machine learning. Also have experience with QlikView, Power BI, Matlab, UiPath, BluePrism and process mining.

Experience

  • Data Scientist at CembraPay
    Aug 2022 - Sep 2023 · 1 yr 2 mos

    Provision model development and documentation (SQL, R, Excel) Optimization and documentation of different models/algorithms (R)

  • Data Scientist at Picanova Group
    Apr 2022 - Jul 2022 · 4 mos

    Demand forecast for multiple different products (SQL, Python) Customer analysis using web tracking log data (Python, Power BI). Production process analysis using log data (SQL, Python, Power BI).

  • Data Scientist in Internal Audit at TietoEVRY
    Feb 2021 - Apr 2022 · 1 yr 3 mos

    Data transformation for process mining of P2P process. Data model enrichment for more detailed insights (R, Power BI, PAFnow). Modeling and analysis of user responsibility data for improvement of segregation of duties purposes using Power BI. Modeling of company asset data for improvement of asset management using Power BI. Ad-hoc data analysis for different internal audit projects

  • KPMG Latvia (4 yrs 3 mos)
    • Assistant Manager
      Oct 2020 - Jan 2021 · 4 mos

      Process optimization by removing multiple manual steps and transfering MATLAB and VBA scripts to R.

    • Senior Advisor
      Jan 2019 - Oct 2020 · 1 yr 10 mos

      - Data analysis, transformation, aggregation, feature engineering and predictive model development to classify debtors using SQL, Power BI and R. - Atomation of various processes using Blue Prism, UiPath and VBA. - Review and calibration of sanction screening system to reduce the number of false positives.

    • Advisor
      Oct 2017 - Dec 2018 · 1 yr 3 mos

      - Client experience data processing and analysis in R to identify the main criteria for recommendation of companies in different industries. - Processing and analysis of log files (process mining) in R to identify the main process flows and possibilities of process optimization. - Application interface design and report development combining data from multiple data sources using BI tools QlikView and Power BI.