Data Scientist

What do we offer?

• We offer both remote and hybrid work opportunities
• Intellinexus is an SME, therefore you get a lot of exposure to different technologies, solutions and techniques during projects.
• Intellinexus supports career development and growth of its employees, therefore time and money is invested to grow their consultants in their careers.

Overall purpose of the role

This is a data science role focusing on acquiring as many quality clients as possible through performance and brand marketing, in the most cost-efficient way possible.

What you'll achieve in the first 12 months:

• We have all the data we need to understand, measure and influence client acquisition: Work across the end-to-end client acquisition journey, from off platform to product conversion, to ensure data is created and consumed in a way that enables you to bring them together to fuel your insights and modelling. Ensure data quality and interoperability is owned at source by working with engineers, architects, and marketers across the ecosystem. Success will be measured by the completeness and quality of data, facilitating a comprehensive view of the client acquisition journey.
• We can reliably and predictably attract the targeted segments of the market onto our platform: Collaborate with campaign managers to iterate on marketing campaigns, refine our segments and establish the most cost-effective way to bring prospective clients to our website to evaluate our products. Produce insights, run experiments, and develop predictive models to optimise channel specific campaign performance and cross channel spend. E.g. What budget and channel allocations do we need to hit our client acquisition and cost of acquisition targets for segment X next quarter? Success will be measured in the number of quality clients acquired through each campaign and efficiency of the spend on it (e.g. client LTV / CAC, campaign ROI).
• Prospective clients have all the relevant information and advice they need to understand their needs and build confidence in our products: Work closely with UX designers and user researchers to create the best landing and evaluation journey experience for prospective clients, optimising content and journey's that are best suited for each segment. Involve user researchers to uncover the why behind the what. E.g. Which web pages contribute most to an acquisition? Do people prefer handholding through our Call Centre Agents or directing their own journey through long form web pages? Success will be measured by conversion of on-platform prospects who enter the qualification funnel and through qualitative surveys about their confidence and experience.
• Once a client decides, the rest of the product onboarding process is so transparent and frictionless, there's almost no drop off: Work with product houses to identify points in the product application and onboarding journey that cause drop off. Work with user researchers to optimise for maintaining transparency and confidence, while addressing any accessibility needs. E.g. Which parts of the onboarding process require the longest time, how can data and ML be used to reduce or even eliminate it? Success will be measured in completion times and rates in this final step of the process.
• As a team, we always know how well we're performing and (roughly) why: Create datasets, metric frameworks and visualisations that together provide information-rich stories about our activities and their performance. These stories should cover everything from the digital marketing channels and partners we're using, to the format and nature of the content (e.g. video vs image, animal photos vs people photos). Leverage computer vision and/or NLP as a service to enrich your data to support this content rich analysis. Success will be measured in the depth of understanding, and minutes it takes, for the team to learn about their key metrics and drivers.

Duties & Responsibilities

• Data Scientists are the people who bring practices from empirical science to life to drive the iterative product development lifecycle. They embed within teams they support, achieving a marriage of minds with their stakeholders and an integration of objectives and workflows. They work across the team with UX researchers, feature engineers, domain experts and business, product, and marketing managers, to create data about user behaviours, discover unmet needs, set metrics that measure performance and design & execute experiments to generate learnings and evidence their success (or lack thereof).
• This is a role where mastery in data wrangling, metric setting, experimentation, machine learning and data storytelling are combined to enable product teams to become learning machines. This requires a thoughtful application of craft across the entire data journey, from data creation to changing our campaigns. Whether it's creating data or generating learnings, training ML models, setting metrics, or collaborating on the roadmap, data scientists are critical partners to the team they embed in

Examples of things a data scientist does while embedded in their commercial team:

• Driving data creation: Establish the first principles and facilitate discussions about how to create data about relevant user behaviours and measure success. Design event log schemas and test their implementation.
• Wrangling data autonomously: Wrangle data into the ideal format for the necessary tool (e.g. Python, Tableau, PowerBI), no how raw and unstructured it and what lake/Lakehouse it's stored in.
• Setting team metrics: Define holistic measurement frameworks that enable the team to explore their performance holistically and work with the team to identify and track the metrics that measure success.
• Producing insights: Identify loosely defined commercial problems and produce meaningful insights that guide iterations of the teams products and go-to-market campaigns.
• Developing ML models: Structure commercial problems into prediction problems by clarifying both what is being predicted and how that enables us to change our products or campaigns to obtain a better result. Develop and implement ML models for the highest priority prediction problems, ensuring outstanding model and product performance.
• Carrying out controlled experiments: Establish the learning goals of their team, crafting hypothesis statements and learning roadmap wherever needed. Design, execute and draw correct conclusions from experiments by confirming integrity of experiment, avoiding common pitfalls, and executing frequentist or Bayesian A/B analysis.
• Segmenting the client base: Using rule and unsupervised learning-based approaches to segment the client base so we can develop a deeper understanding of customers and build better products for them.
• Creating impactful visualisations: Produce data and interactive visualisations that enable the whole team to address their recurring reporting and insight needs, without your direct support.
• Partnering for impact: Partner with stakeholders to make sure they gain the confidence they need to drive changes in the product roadmap or ask for further insight.
• Evolving the learning mindset of the team: Create a culture of insights and learning, where everyone obsesses about their success metrics and are constantly testing and learning to better their performance.

Interested?

Our aim is to help you build a successful career with us

We're all about building strong, lasting relationships with our employees. We know that you have hopes for your future – your career, your personal development, and for achieving great things. We pride ourselves in helping our employees to realize their worth. Through Intellinexus and its clients, we provide many opportunities for growth and development.

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