Typical duties and responsibilities for Data scientist position may include but not limited to: 

• Collect, clean, and preprocess large datasets to prepare them for analysis, ensuring high data quality.

• Develop, implement, and fine-tune machine learning models using techniques including time-series forecasting, deep learning, and NLP.

• Work with cross-functional teams to define data requirements and develop data-driven solutions.

• Collaborate closely with data engineers to build robust data and model pipelines.

• Develop and maintain data models and data schemas.

• Visualize and communicate data insights to stakeholders.

• Identify and communicate data quality issues and work to resolve them.

• Stay up to date with the latest data analysis and machine learning tools and techniques.

• Knowledge sharing and culture building as we work in team structure; it is very essential to have the spirit of sharing the knowledge.

• Keep aligned with HARRI’s team(s) coding and design standards.

• Ability to communicate and work well with others.

• Ability to deliver on time keeping a high quality of work.

• To quickly become an expert in our tech stack, specifically the systems relating to your role. Requesting training when required.

• Check in with your line manager and update on your progress.

• To live and breathe the Harri Company values.

• Comply with Harri Health & Safety policy & regulations.
  

• Bachelor's or master's degree in computer science or related field.

• 2+ years of development experience

• 1+ years practical data science / machine learning experience that includes building operational ML models

• Have exceptional mathematical and statistical skills.

• Can verify the quality of data.

• Have extensive hands-on experience in Python for data science, with strong skills in Pandas for data manipulation, feature engineering, and data extraction.

• Have excellent data observation and analytical skills

• Have proficiency in data visualization libraries such as Matplotlib, Seaborn, Plotly, and ggplot.

• Have a deep understanding of statistical modeling techniques.

• Have comprehensive and deep knowledge of ML algorithms, frameworks, libraries, data structures, data modeling, and software architecture; scikit-learn, XGBoost, PyTorch, Keras, and Tensorflow.

• Have experience with Large Language Models (LLMs), such as GPT, BERT, or similar, including fine-tuning and deploying them for various tasks.

• Have practical NLP experience using embedding, attention-based models, and LLMs.

• Can verify the quality of trained models, best use of evaluation metrics, confusion matrix and validation curves.

• Can collaborate with data engineers to build data and model pipelines.

• Can test and deploy models to Production.

• Can well communicate and explain processes/services to product managers and any non-technical experts.

• Can research and implement best practices to improve the existing models in terms of data points, algorithms, and/or infrastructure.

• Have a keen sense of the importance of Object-Oriented development and design patterns.

• Have proven work experience in SQL and relational databases.

• Experience with AWS components especially EC2, S3, SageMaker is a great plus.

• Proficiency with Git for version control.

• Strong problem-solving and analytical skills.

• Experience working in Agile environments.

• Experience in JIRA management tool, defect cycle, defect priority, and triage.

• Excellent verbal and written English communication skills.

• Excellent communication and collaboration skills.

Typical duties and responsibilities for Data scientist position may include but not limited to: 

• Collect, clean, and preprocess large datasets to prepare them for analysis, ensuring high data quality.

• Develop, implement, and fine-tune machine learning models using techniques including time-series forecasting, deep learning, and NLP.

• Work with cross-functional teams to define data requirements and develop data-driven solutions.

• Collaborate closely with data engineers to build robust data and model pipelines.

• Develop and maintain data models and data schemas.

• Visualize and communicate data insights to stakeholders.

• Identify and communicate data quality issues and work to resolve them.

• Stay up to date with the latest data analysis and machine learning tools and techniques.

• Knowledge sharing and culture building as we work in team structure; it is very essential to have the spirit of sharing the knowledge.

• Keep aligned with HARRI’s team(s) coding and design standards.

• Ability to communicate and work well with others.

• Ability to deliver on time keeping a high quality of work.

• To quickly become an expert in our tech stack, specifically the systems relating to your role. Requesting training when required.

• Check in with your line manager and update on your progress.

• To live and breathe the Harri Company values.

• Comply with Harri Health & Safety policy & regulations.
  

• Bachelor's or master's degree in computer science or related field.

• 2+ years of development experience

• 1+ years practical data science / machine learning experience that includes building operational ML models

• Have exceptional mathematical and statistical skills.

• Can verify the quality of data.

• Have extensive hands-on experience in Python for data science, with strong skills in Pandas for data manipulation, feature engineering, and data extraction.

• Have excellent data observation and analytical skills

• Have proficiency in data visualization libraries such as Matplotlib, Seaborn, Plotly, and ggplot.

• Have a deep understanding of statistical modeling techniques.

• Have comprehensive and deep knowledge of ML algorithms, frameworks, libraries, data structures, data modeling, and software architecture; scikit-learn, XGBoost, PyTorch, Keras, and Tensorflow.

• Have experience with Large Language Models (LLMs), such as GPT, BERT, or similar, including fine-tuning and deploying them for various tasks.

• Have practical NLP experience using embedding, attention-based models, and LLMs.

• Can verify the quality of trained models, best use of evaluation metrics, confusion matrix and validation curves.

• Can collaborate with data engineers to build data and model pipelines.

• Can test and deploy models to Production.

• Can well communicate and explain processes/services to product managers and any non-technical experts.

• Can research and implement best practices to improve the existing models in terms of data points, algorithms, and/or infrastructure.

• Have a keen sense of the importance of Object-Oriented development and design patterns.

• Have proven work experience in SQL and relational databases.

• Experience with AWS components especially EC2, S3, SageMaker is a great plus.

• Proficiency with Git for version control.

• Strong problem-solving and analytical skills.

• Experience working in Agile environments.

• Experience in JIRA management tool, defect cycle, defect priority, and triage.

• Excellent verbal and written English communication skills.

• Excellent communication and collaboration skills.