Growth Manager
About the job
- A Bachelor's degree in Marketing, Business, Data Analytics, or a related field is required.
- Four to five years of experience in performance marketing, digital growth, engagement marketing, or lifecycle marketing is required.
- Hands-on experience with growth automation tools, especially MoEngage, Plotline, CleverTap, Braze, or similar tools, is required.
- Ability to extract insights from campaign dashboards and propose data-driven actions is required.
- Familiarity with social media campaigns, in-app journeys, and customer relationship management workflows is required.
- Proficiency with spreadsheets, basic dashboards, and analytics tools is required.
- Ability to create concise, farmer-friendly messaging is required.
- Lead and scale performance marketing across Google Ads, Meta Ads, and other channels, managing campaigns, targeting, budgets, and optimization to maximize return on ad spend and customer acquisition cost.
- Drive funnel growth through data-driven paid campaigns, partnerships, and influencer activations, with clear attribution to installs and activations.
- Plan and execute seasonal and event-based acquisition campaigns aligned with crop cycles, festivals, and local trends, using A/B testing to improve conversions.
- Identify and manage regional influencers and creators with a performance-first approach, tracking cost per install, engagement quality, and retention.
- Work with the Global Growth Lead to adapt global growth playbooks for new and existing markets.
- Translate engagement insights into localized campaigns for Kenya, Ethiopia, Nigeria, and other focus regions.
- Share performance learnings and regional best practices with global and country teams.
- Create and optimize MoEngage flows and campaigns to convert new app users into active farmers.
- Build and manage engagement workflows using MoEngage for push notifications, WhatsApp and SMS campaigns, segmentation, and A/B testing, and Plotline for in-app reels and stories, spotlight journeys, and feature-discovery nudges.
- Analyze campaign performance, including click-through rate, engagement rate, session depth, and conversions, and iterate on creatives, copy, and targeting.
- Optimize retention and re-engagement using insights from paid ads, influencer performance, and in-app behavior.
- Develop targeted reactivation journeys to win back dormant users and reduce churn.
- Coordinate paid ads, social media, and influencer campaigns for consistent messaging and maximum impact.
- Track growth metrics such as acquisition rate, activation percentage, day 7/day 14/day 30 retention, churn, and click-through rate.
- Use MoEngage Analytics, dashboards, and other data tools such as Google Analytics, Sheets, or BigQuery to generate insights.
- Work with the Experiments Team to run A/B experiments to improve notification timing, in-app experiences, and creative performance.
- Collaborate with content teams and local creators to design farmer-friendly notifications, short videos, and social posts that drive engagement.
- Adapt and translate campaigns into local languages, including Swahili, Amharic, and Oromo.
- Coordinate with design and performance marketing teams to align growth experiments with brand and regional campaigns.
- Experience in agtech, edtech, or other consumer-facing digital platforms in emerging markets.
- Familiarity with digital ecosystems and local farmer behavior patterns.
- Basic SQL or experience with data visualization tools such as Looker, Tableau, or Data Studio.
- Fluency in one or more of Hausa, Swahili, Amharic, or Oromo.
About the company
Digital Green organizes and supports local farming networks and agronomy resources to give practical, actionable farming advice to smallholder farmers in their own languages. The product works by connecting farmers with local agronomists and extension workers, collecting field data and context, and using AI to translate and tailor recommendations to each farmer’s crop, season, and situation. Advice is delivered through accessible channels and in the farmer’s language, enabling real-time or timely guidance. The company stands out by tying together community networks, on-the-ground agronomy expertise, and AI-driven personalization, rather than relying on a single channel or generic content. The goal is to boost farm productivity and livelihoods by making expert guidance easy to understand and use for farmers with limited resources.
Company Size
51-200
Company Stage
Grant
Total Funding
$1.3M
Headquarters
San Francisco, California
Founded
2008
Get referred to Digital Green
See people who can refer or advise you
Simplify's Take
What believers are saying
- Gates Foundation committed $1.95 million in August 2026 for agriculture and financial inclusion.
- June 2026 usage reached 8M answers, and 70% of active users applied advice.
- Brand refresh and new website in June 2026 signal expanded fundraising and hiring momentum.
What critics are saying
- OpenAI integration risk: Microsoft and OpenAI can raise prices or change APIs in 2027.
- Grant dependence is acute; $1.95 million covers 35 months, not durable self-funding.
- Local ministries can copy validated content and bypass Digital Green by 2028.
What makes Digital Green unique
- Since 2008, Digital Green built farmer-grounded video libraries and localized advisory workflows.
- June 2026 FarmerChat serves 1.6M farmers across six countries in 16 languages.
- Microsoft and OpenAI partnerships give Digital Green multimodal AI, Azure, and distribution.
Help us improve and share your feedback! Did you find this helpful?
Benefits
Remote Work Options
Growth & Insights and Company News
6 month growth
-4%1 year growth
-1%2 year growth
0%Microsoft is teaching AI to speak Dholuo and Kikuyu, starting with Kenyan farmers. Generative AI has a language problem. While models like GPT-4 are proficient in English, their performance drops off a cliff when tasked with low-resource languages or cultural nuances specific to the "global majority" - the projected 85 percent of the world's population that lives outside the West. The issue isn't just translation; it's that the underlying training data simply doesn't exist for many communities. Microsoft is attempting to solve this infrastructure gap with a new initiative called Project Gecko. Led by Microsoft Research Africa in Nairobi, alongside teams in India and the US, the project is building Small Language Models (SLMs) designed to run on low-cost devices, specifically tailored for local needs. The project is starting with agriculture in Kenya and India, debuting a new multimodal AI agent that can watch videos, listen to vernacular speech, and answer farmers' questions with verified accuracy. The "hallucination" Problem in farming. The core problem Project Gecko aims to solve is the disconnect between generic AI models and on-the-ground reality. If a smallholder farmer in Nyeri asks a standard LLM about crop diseases in Kikuyu, the model might hallucinate an answer or provide advice relevant to US industrial farming, not local soil contexts. To fix this, Microsoft has partnered with Digital Green, a global development organization that maintains a library of over 10,000 agricultural videos in more than 40 languages. Previously, this data was siloed and hard to search. Project Gecko processes this data using a new system called the MultiModal Critical Thinking Agent (MMCTAgent). Unlike a standard chatbot that just predicts the next word in a sentence, the MMCTAgent analyzes speech, images, and video content simultaneously. When a farmer asks a question, the agent doesn't just generate text; it reasons across the available media and retrieves the specific video timestamp where the solution is demonstrated. For example, a farmer can ask a verbal question in Kikuyu. The system processes the audio, finds the relevant visual answer in Digital Green's database, and responds with text, audio, and a video clip. Microsoft says field studies in Kenya and India showed this method significantly improved user trust compared to generic AI, largely because the AI "shows its work" by grounding answers in real video content rather than abstract training data. Small models and local data. The technical backbone of Project Gecko relies on Small Language Models (SLMs) rather than massive, cloud-heavy LLMs. There are two reasons for this. First, internet bandwidth and device capabilities in rural Kenya are limited; SLMs require less computing power. Second, existing large datasets for languages like Kalenjin or Maa are virtually non-existent, making it impossible to train massive models effectively. The Project Gecko team had to build the tools for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) from scratch. They compiled a dataset of 3,000 hours of crowd-sourced Kenyan speech to train and fine-tune these models. Currently, the system supports Swahili, Kalenjin, Dholuo, Maa, Kikuyu, and Somali. "Agriculture has very specific terms, which may change from language to language, and even district to district," says Tanuja Ganu, Director of Research Engineering at Microsoft India. "All those domain-specific nuances need to be understood." Open source and future plans. Microsoft is positioning this as a foundation for broader application, not just a proprietary tool. The MMCTAgent is available now on Azure AI Foundry Labs, and the code has been open-sourced on GitHub. The company is also creating a public leaderboard to benchmark how well AI models perform in African languages, attempting to create a standard for accuracy that currently doesn't exist. While agriculture is the test bed due to its economic importance in Kenya - where it contributes significantly to GDP - Microsoft plans to replicate this "design pattern" for healthcare and education. The idea is to create a playbook for developers to build domain-specific AI tools that don't rely on Silicon Valley-centric data. The team is currently using insights from 130 farmers to refine features like clarifying questions and peer-to-peer knowledge sharing. "Building AI systems from the ground up shaped by the knowledge, languages, and modalities of the global majority yields more innovative, useful solutions for a great number of people," says Ashley Llorens, VP and Managing Director of the Microsoft Research Accelerator. For now, the focus remains on proving that a farmer in Nyeri can get an answer as accurate as a software engineer in Seattle.
In collaboration with One Acre Fund, Digital Green will develop a groundbreaking AI project, Farmer Chat, aimed at supporting small-scale farmers in underserved areas of Sub-Saharan Africa.
The Food and Agriculture Organization (FAO), Digital Green, and the University of the Philippines Los Baños College of Development Communication (UPLB CDC) have released recently Participatory video in agrifood systems and digital environments: A practitioner's guide.
Under the support of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, the International Potato Center (CIP) in collaboration with Digital Green and other partners has embarked on a mission to promote Conservation Agriculture and enhance Climate Resilience.
Digital Green, a global development organisation has joined forces with generative AI startup Gooey.AI to introduce Farmer.CHAT, an innovative solution aimed at addressing the challenges faced by farmers who are at the forefront of climate change and water security issues.