Analytics Engineer

Data Analyst

About us

At AG, we are passionate about unlocking the value of data.

Are you ardent about working on challenging business problems that can be solved by the intersection of people, data, technology and AI? Do you want to be part of a fast-growing world-class team of high performing AI experts who strives to go the extra mile to enable the bold digital visions of our clients?

We deliver a comprehensive suite of AI solutions and services, including Natural Language Processing, GenAI, LLMs, Predictive Analytics, and Machine Learning, among others.

About the team

People at AG have deep technical expertise , we’re curious, and enjoy complex problem solving . We’re interlocked by our core values and are continuing to build on and further develop our high performing team to grow our nimble and trusting work environment. We offer an environment conducive to those with a growth mindset, who have a passion for data and leveraging the latest AI technologies and methodologies .

About the opportunity

We are seeking a talented and motivated Data Analyst with a passion for advanced data analytics to join our Data Anal ytics champion team.

In this role you are responsible for transforming data in to deep bu siness processor insights . Your work involves collecting, processing, and interpreting data. Furthermore, you will analyze data using statistics, algorithms, machine-learning, innovative problem solving and software development to generate data-driven findings to help guide our client’s business decisions.

• Data Collection and Processing : responsible for collecting data from various sources (such as querying relational databases with SQL), interpreting patterns and trends from these data sets, and cleaning and validating the data to ensure accuracy, completeness, and uniformity.
• Data Analysis : using business analysis, statistical techniques and advanced algorithms to interpret data sets, and analyze results that help the organization make data-driven decisions. This often includes building models to better understand data, and making use of software development in languages such as Python, to develop the solution.
• Reporting : creating detailed summaries, dashboards and visual presentations to communicate their findings to the client organization. These reports may include graphs, charts, and tables to present data in a way that's easy to understand for both technical and non-technical audiences.
• Experiment Design: develop, design and execute experiments, perform A/B tests with hypothesis testing, use data analytics to extract learnings, and grow the organization’s knowledge to help them improve their business and decision making.
• Strategy Development & Problem Solving : helping clients understand the impact of business decisions, measure performance, and identify opportunities for improvement or growth. Identifying trends and patterns that can help an organization solve problems, make informed decisions, and pursue new opportunities
• The specific responsibilities of the individual that performs Advanced Data Analytics can vary based on the client and industry. Some may focus more on technical aspects, like writing code and performing advanced analytics, while others may be more focused on business aspects, like strategy development and decision-making support.

Your skills

Analytics

• Diagnostic Analysis: Go beyond surface-level observations by identifying patterns, anomalies, and relationships within the data. This involves using statistical techniques to drill down into the data and understand the root causes of specific outcomes. Skills in hypothesis testing, correlation analysis, and segmentation are essential.
• Descriptive Analysis: Summarize past data to understand what has happened in a business context. This involves aggregating and organizing data into meaningful metrics and KPIs that help in understanding historical trends and performance. Proficiency in data aggregation tools, reporting dashboards, and SQL is crucial.
• Predictive Analysis: Anticipate future outcomes by leveraging statistical models and machine learning algorithms. This requires a strong understanding of predictive modelling techniques such as regression analysis, time series forecasting, and classification models. Familiarity with tools like Python (specifically libraries like scikit-learn) is beneficial.
• Prescriptive Analysis: Extend analytics into actionable recommendations by simulating various scenarios and outcomes. This includes optimization techniques, decision analysis, and scenario planning. Proficiency in optimization algorithms, Monte Carlo simulations, and decision trees can significantly enhance prescriptive analysis capabilities.
• Causal Analysis: Establish cause-and-effect relationships within data to understand the impact of specific actions or interventions. This often involves advanced statistical techniques like A/B testing, randomized control trials, d ifference in differences analysis and propensity score matching.
• Exploratory Data Analysis (EDA) : Before formal analysis, conduct EDA to uncover patterns, spot anomalies, test hypotheses, and check assumptions. This step is crucial for identifying the best approach for further analysis. EDA requires strong skills in data visualization and familiarity with tools like Python (e.g., pandas, seaborn, matplotlib).
• Data literacy: Derive meaning from data and communicate that meaning to others. Data literacy competencies include the knowledge and skills to read, analyse, interpret, visualize and communicate data as well as to understand the use of data in decision-making. Cleaning the data to maintain its quality, by removing outliers and duplicates, correcting errors, identifying and fixing data ingestion issues.
• Data Collection: Collect data through various collection methods. This could include pulling data from clients/3rd parties, conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.
• Interpretation | Visualization | Presentation: Finding patterns or trends in data to be able to translate the numbers into business insight by telling the story. Presenting data findings by putting together visualizations like charts and graphs, writing reports, and presenting information to stakeholders.
• Software Development : An ability to write Python code to perform analysis, and to write SQL to query databases. Ability to work in notebooks and scripts and contribute to software projects. Good software development practice such as committing code to a repository and writing data tests and so ftware tests. Proficiency in Excel is advantageous .
• [OPTIONAL] ML (Machine Learning): While not everyone will find themselves working on machine learning projects, having a general understanding of related tools and concepts will give you an edge. Some advanced analytics techniques make use of Machine Learning (such as outlier removal and dimensionality reduction to name just two) and these skill s will be very welcomed

Life at AdvanceGuidance

• We are a committed team who values diversity and hard work, whilst on the continuous quest to be the best we can be
• Our hybrid work policy gives you the freedom to navigate your workdays between home and the office. We believe we are ‘stronger together’ and see each other on Wednesday and Fridays
• We are pretty serious about your personal and professional development and offer access to a diverse range of learning platforms
• Communication allowance for your mobile phone and internet bills
• A collaborative, supportive work community, with regular social events
• Weekly Free lunch