This is a remote position.
About Us
DataTeams help Technology Companies find the most skill specific AI enabled Data Professionals.
What You Will Help Us With:
Unlocking Insights from Complex Data:
You will enable our interdisciplinary teams and clients to extract meaningful insights from vast, multi-terabyte datasets. By developing and optimising data ingestion pipelines, you will support the integration of geospatial and remote sensing data, which will drive our innovative product offerings and nature-based projects.
Technical Leadership and Innovation:
In this role, you will provide technical leadership for data science projects, building robust data pipelines that serve as core assets across various teams. Your leadership will help connect product value across departments by designing scalable systems and leveraging cutting-edge technologies to meet the demands of complex data processing.
Strategy and Vision for Data Systems:
You will help shape the strategic direction for data infrastructure, collaborating closely with teams to develop solutions that drive value. Through your expertise in advanced analytics and data science, you will contribute to decision-making and innovation across our data ecosystem.
Mentorship and Best Practices:
You will mentor and advocate for best practices in data science, from model development to data pipelines. Your guidance will help elevate the overall performance of the team, ensuring that data models and processing pipelines are efficient, scalable, and aligned with the latest industry standards.
Hands-On Contributions:
Your role will include direct involvement in coding and developing data-driven systems that empower both engineering and scientific research. You will apply data science techniques to produce high-quality insights for critical projects, such as forest carbon monitoring, while also optimising analytical methods for processing large geospatial and remote sensing datasets.
Experience & Skills We’re Looking For:
- Design, develop, and implement machine learning models and algorithms to address complex business challenges.
- Analyse large and diverse datasets to extract actionable insights, identify trends, and provide data-driven recommendations.
- Collaborate with cross-functional teams, including data engineers, analysts, and business stakeholders, to understand business objectives and translate them into data solutions.
- Develop predictive models, perform hypothesis testing, and apply statistical analysis to drive business outcomes.
- Work with structured and unstructured data from various sources to create comprehensive and accurate data models.
- Optimise machine learning algorithms and data processing techniques for improved accuracy, scalability, and efficiency.
- Build and maintain data pipelines for the seamless integration of data sources into the analytics environment.
- Create data visualisations and dashboards to effectively communicate insights to both technical and non-technical audiences.
- Stay updated on the latest industry trends, tools, and technologies in data science, artificial intelligence (AI), and machine learning (ML).
- Document methodologies, processes, and best practices for future reference and knowledge sharing.
Requirements:
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Strong proficiency in programming languages such as Python or R, with experience in data manipulation and analysis. - Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and statistical modelling.
- Experience with data visualisation tools such as Tableau, Power BI, or Matplotlib.
- Proficiency in SQL and working knowledge of relational databases (e.g., MySQL, PostgreSQL, SQL Server).
- Strong problem-solving and analytical skills, with the ability to apply advanced statistical techniques to real-world problems.
- Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, particularly in machine learning and data analytics services.
- Ability to work collaboratively in a team environment, as well as independently on complex projects.
- Excellent communication skills, with the ability to present technical findings to both technical and non-technical stakeholders.
Preferred Qualifications:
- IBM Data Science Professional Certificate
- Microsoft Certified: Azure Data Scientist Associate
- Certified Analytics Professional (CAP)
- Data Science Council of America (DASCA) Certifications
- TensorFlow Developer Certificate
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