Veri Bilimci

Padran Inc.

Fatih

Hybrid

TRY 300,000 - 540,000

Full time

3 days ago
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Benefits offered by this job

Remote work
Hybrid work model

Job summary

Padran Information Technologies is seeking a Data Scientist to join our consulting team in Turkey. You will work on projects for leading companies, building models, analyzing sensor data, and delivering actionable insights.

Strong time series and IoT data experience are essential for predictive maintenance and operational optimization. We value Python expertise, experience with ML frameworks, and English communication skills.

Qualifications

  • Bachelor’s or Master’s in Data Science, Statistics, Engineering, CS or related field.
  • 4+ years hands-on data science with model building and analysis.
  • Proficient Python for data manipulation and model development.
  • Experience with time series modeling and anomaly detection.
  • Familiarity with IoT, PLCs/SCADA data and manufacturing analytics.
  • Strong knowledge of data lakes, warehousing and deployment in production.

Responsibilities

  • Develop and deploy time series models for predictive maintenance and process optimization.
  • Work with sensor data (high-frequency, irregular samples) from IoT devices.
  • Collaborate with clients to translate business problems into analytics solutions.
  • Advance data processing, cleaning, and exploration of machine-generated data.

Skills

Python
Time series
Sensor data
ML frameworks
IoT data
Forecasting
Data analysis
English proficiency

Education

Bachelor’s or Master’s in Data Science / Statistics / Engineering / CS

Tools

TensorFlow
PyTorch
Scikit-learn
SQL
Data Lakes

Job description

As Padran Information Technologies, we are looking for teammates who are focused on growth and success! In this position, you will work for our clients that we provide consultancy and you will take part in projects in Turkey's leading companies.

We are looking for a Data Scientist who meets the following qualifications for our consulting business partner.

Bachelor or Master’s in Data Science, Statistics, Engineering, Computer Science, or a related field.

  • 4+ years of hands‑on experience in data science, with demonstrated expertise in model building and data analysis.
  • Proficiency in Python for data manipulation, analysis, and model development. Time Series Modeling: Proficiency with ARIMA, LSTM, Transformer-based architectures, and anomaly detection models for time series data. Sensor Data Expertise: Hands‑on experience working with high‑frequency, irregularly sampled data from sensors. Manufacturing & Industrial Sensor Data: Familiarity with data originating from IoT devices, PLCs (Programmable Logic Controllers), SCADA systems, or similar sources. Data Processing: Skilled in processing, cleaning, and analyzing machine‑generated data such as vibration, temperature, and pressure measurements. Application Areas: Practical experience in predictive maintenance, anomaly detection, or process optimization projects.
  • Familiarity with database systems, data lakes, and warehousing solutions.
  • Strong expertise in time series analysis and forecasting techniques and anomaly detection.
  • Proficiency in ML frameworks (TensorFlow, PyTorch, Scikit‑Learn) and model deployment in production environments.
  • Prior experience in manufacturing or energy is advantageous, with a strong understanding of processes and industry‑specific challenges.
  • Knowledge of edge computing and IoT integration for smart factories is a plus.
  • Strong understanding of business processes and the application of advanced analytics to solve industry‑specific problems.
  • Verbal and written communication skills in English.
Why Padran Information Technologies?
  • - Opportunity to work with leading companies in Turkey
  • - Opportunity to use industry‑leading technologies with our business partners Microsoft, IBM, AWS and Open Text
  • - Career development and certification opportunities as an ISTQB accredited training center
  • - Flexible and efficient working environment with Remote / Hybrid working model
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