Data Scientist

1 week ago

Solna Municipality, Sweden MatchPoint Full time

We are seeking a highly skilled and motivated Data Scientist to join our team. The ideal candidate will have a strong background in data analysis, deep learning, and GenAI. This role involves extracting meaningful insights from complex datasets and translating them into actionable strategies that drive business growth. You will collaborate with cross-functional teams to design and implement data-driven solutions to solve real-world problems.

Job Experience Requirements:

Location: Solna, Sweden

Hybrid

Qualifications:

· Education: Master's in data science, Statistics, Computer Science, or a related field.

· Experience: 3+ years of experience in data science, analytics, or a related field.

· Technical Skills:

o Proficiency in programming languages such as Python or R.

o Good understanding of statistical principles for ML applications

o Experience with data manipulation and analysis using libraries such as pandas, NumPy, and SciPy.

o Exceptional knowledge of Deep Learning techniques, model architectures, and parameter fine-tuning

o Very good knowledge of GenAI with good understanding of foundation models and transformer architecture

o Familiarity with data visualization tools (e.g., Matplotlib, Seaborn).

o Experience with SQL and relational databases.

o Knowledge of big data technologies (e.g., Hadoop, Spark) is a plus.

· Soft Skills:

Excellent problem-solving abilities, strong communication skills, and the ability to work effectively in a team environment.

· Additional Skills (Preferred):

o Experience with cloud platforms (e.g., AWS, Google Cloud, Azure).

o Familiarity with version control systems (e.g., Git).

o Experience in genomics

Job Responsibilities:

· Data Analysis: Analyze large, complex datasets to identify trends, patterns, and insights that inform business decisions.

· Model Development: Develop, implement, and validate predictive models and machine learning algorithms to solve various business problems.

· Data Cleaning: Preprocess and clean data to ensure its quality and integrity before analysis.

· Visualization: Create data visualizations and dashboards to communicate findings to stakeholders in a clear and concise manner.

· Collaboration: Work closely with cross-functional teams, including engineering, product management, and marketing, to understand their data needs and provide relevant insights.

· Reporting: Generate comprehensive reports and presentations that summarize key findings and recommendations.

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