Data Scientist

Helic & Co; United States; On-site; USD 90,000 - 140,000; Full time

Job summary

Helic & Co is seeking a Data Scientist to analyze complex data, build predictive models, and translate findings into actionable business insights. You will work with cross-functional teams to improve products, processes, and performance through data-driven solutions.

The role emphasizes collaboration with engineering, product, and analytics teams, development of dashboards, and ongoing model monitoring to adapt to changing datasets and business needs.

Qualifications

• Bachelor’s or Master’s degree in a related field.
• 2+ years of experience in data science or analytics.
• Proficiency in Python or R.
• Experience with SQL and relational databases.
• Strong statistics, probability, ML, and data modeling knowledge.
• Experience with data visualization tools.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently while collaborating with cross-functional teams.

Responsibilities

• Collect, clean, transform, and analyze data from multiple sources.
• Develop and implement statistical models and ML algorithms.
• Identify patterns, trends, and opportunities; translate into insights.
• Build and evaluate data models for forecasting, classification, segmentation.
• Create dashboards and visualizations to communicate findings.
• Collaborate with engineering, product, business, and analytics teams.
• Monitor model performance and improve analyses as datasets evolve.
• Document methodologies, analyses, and results for reproducibility.

Skills

Analytical thinking

Problem solving

Communication

Cross-functional collaboration

Data visualization

Python/R

SQL

Education

Bachelor’s or Master’s in a related field

Tools

Scikit-learn

TensorFlow

PyTorch

Spark

Hadoop

Tableau

Power BI

AWS

SQL databases

Job description

We are seeking a skilled Data Scientist to analyze complex datasets, develop predictive models, and generate actionable insights that support business decisions. The successful candidate will work with cross-functional teams to identify trends, solve business problems, and improve products, processes, and overall performance through data-driven solutions.

Key Responsibilities

• Collect, clean, transform, and analyze structured and unstructured data from multiple sources.
• Develop and implement statistical models, machine learning algorithms, and predictive analytics solutions.
• Identify patterns, trends, and opportunities within large datasets and translate findings into actionable insights.
• Build and evaluate data models to support forecasting, classification, segmentation, and business decision-making.
• Create dashboards, reports, and visualizations to communicate complex findings clearly to technical and non-technical stakeholders.
• Collaborate with engineering, product, business, and analytics teams to define data requirements and solve business problems.
• Monitor model performance and improve analytical solutions as business needs and datasets evolve.
• Document methodologies, assumptions, analyses, and results to support reproducibility and informed decision-making.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 2+ years of experience in data science, data analytics, machine learning, or a related field.
• Strong proficiency in Python or R for data analysis and machine learning.
• Experience with SQL and relational databases.
• Solid understanding of statistics, probability, machine learning, and data modeling concepts.
• Experience working with data visualization and reporting tools.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently while collaborating effectively with cross-functional teams.

Preferred Skills

• Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Experience with big data technologies such as Spark or Hadoop.
• Familiarity with Tableau, Power BI, or similar business intelligence tools.
• Experience with A/B testing, experimentation, and advanced statistical analysis.
• Knowledge of data engineering concepts, ETL pipelines, and data warehousing.
• Understanding of MLOps, model deployment, monitoring, and lifecycle management.
• Ability to communicate technical findings effectively to senior leadership and business stakeholders.